26 results about "Complex field" patented technology

The present specification relates to a technique by which a hologram generation device generates a hologram with reduced speckle noise by optimizing a carrier according to a specific distance. A method by which a hologram generation device generates a hologram, according to an embodiment of the present specification, comprises the steps of: setting a carrier by initializing an arbitrary complex field having a random phase and a random amplitude; removing a specific band component from the frequency band of the set carrier so as to transform the set carrier; propagating the transformed carrier for each specific distance, which is a depth plane in which a target object is present; setting a loss function for each specific distance on the basis of a target uniform amplitude at the specific distance and the amplitude of the carrier propagated to the specific distance; and updating the propagated carrier for each specific distance on the basis of the loss functions set for each specific distance.

The application discloses a ginseng flower and seed lightweight field detection method based on RT-DETR improvement, and belongs to the field of agricultural intelligent detection and deep learning application.The application takes RT-DETR as a baseline model, and makes targeted improvements from three aspects of backbone network, backbone-neck feature interaction and neck network downsampling in view of problems such as complex environmental interference, small target feature sparsity and difficulty in edge deployment of the model in field detection of ginseng flower, green seed and red seed.The model lightweight design is realized, the anti-interference ability and detection precision of the model to the complex field environment are improved, and the real-time detection demand of edge equipment in the field is met.The training of the improved model is completed, the model is deployed on a JetsonOrinNX edge computing platform, end-to-end real-time detection of ginseng flower, green seed and red seed in the complex field environment is realized, core visual technology support is provided for intelligent picking and field fine management of ginseng, and the application has important engineering application value for promoting the automatic and intelligent upgrading of the ginseng industry.

This invention proposes a neural network model enhancement and electronic device for small target defect detection in oilfield inspection images under complex field scenarios. It addresses the problems of severe feature degradation, scarcity of real samples, poor spatial localization ability, low anchor box matching degree, and limited computing power at the edge in small target defect detection in oilfield inspection images under complex field scenarios. This invention constructs synthetic and real datasets through dual-source data processing; performs four-fold super-resolution amplification of the input data through residual dense block concatenation and sub-pixel convolution, and introduces a generative adversarial network detail enhancement branch to generate super-resolution reconstructed images; extracts multi-scale basic features through a lightweight backbone; generates coordinate attention weights through global pooling of height and width dimensions to enhance the spatial features of small targets; inputs the enhanced feature map into a defect detection layer embedded with anchor boxes specific to small targets for classification and regression; and optimizes model training using a hard sample mining loss function.

1. The name of the design product: leveling system. 2. The use of the design product: for vehicles and equipment leveling in complex field environment. 3. The design points of the design product: in the appearance shape of the product. 4. The picture or photo that best indicates the design points: perspective view 1.

The present application provides a kind of combine harvester to be harvested crop plant height and swath measurement method and system and harvester, comprising the following steps: step S1, the depth image and RGB image of the front area to be harvested are synchronously collected, and the depth image and RGB image are aligned processing;Step S2, extract the region of interest, identify crop ear through image processing, and calculate plant height in combination with depth information;S3, using the instance segmentation model based on improved deep learning, identify the boundary line of the area that has been harvested and the area that has not been harvested in RGB image;S4, based on the pixel coordinates of boundary line, corresponding depth value and camera parameter, the real-time swath width is obtained by coordinate conversion calculation;S5, obtain inertial measurement unit data, dynamically estimate and correct the attitude angle of camera through sensor fusion algorithm, and the attitude angle is used for the calculation correction of plant height and swath width.The present application realizes the synchronous, real-time and robust measurement of crop height and swath width in complex field environment.

This invention relates to the field of B2B foreign trade website building technology, and in particular to a content model construction and unified engine execution method, system, and device. The method includes: the client configuring fields of the content model; the server constructing the fields into a tree-like field structure according to hierarchical relationships, storing the tree-like field structure as metadata, and assembling a model definition object based on the read metadata; the client recursively rendering the form interface based on the model definition object; the server generating data validation rules based on the configuration parameters in the metadata, and validating the form data submitted by the client based on the data validation rules; the server storing the validated form data according to the field attributes in the metadata, and performing state management, soft deletion, and sorting processing on the content data. This invention can solve the problems of high coupling between model and code, complex field nesting, difficulty in expansion, code redundancy, and difficulty in ensuring consistency of state across multiple modules in existing technologies.

The application discloses an anti-interference type thermal power coal quality multi-parameter online monitoring system and relates to the technical field of thermal power generation. The application solves the technical problem that the data processing method of the prior art often adopts simple filtering or mean processing, the anti-interference capability is weak, the feature extraction is not pure, and the accuracy of the monitoring result is low. The application obtains a normalized fusion input vector by processing multi-source heterogeneous original data, and performs anti-interference feature extraction on a coal quality deep feature vector. The application generates a monitoring result corresponding to a plurality of parameters based on the coal quality deep feature vector, dynamically identifies and suppresses an interference component introduced by a change in an operation state in original signals by using working condition data, thereby refining a pure and robust coal quality deep feature vector, replacing a passive processing mode such as fixed threshold filtering or sliding average, improving the purity of feature extraction and the robustness to a complex field environment, and further improving the accuracy and stability of the monitoring result.

The application discloses a blueberry counting and yield estimation method and system for complex field environment, and belongs to the technical field of agricultural computer vision, which comprises the following steps: collecting a video of field blueberries as a data set, and performing labeling and data enhancement; an improved YOLOv8n detection and classification model is used to process the input blueberry video and output a detection result; the detection result is input into an improved OC-SORT tracking algorithm to track the blueberry target across frames and maintain the uniqueness of its identity; a double-region counting method is used to count the tracking trajectory to obtain a counting result, thereby realizing yield estimation; the maturity classification function is integrated into the detector, and an optimized tracking algorithm is used to deal with severe occlusion and camera shaking, thereby solving the problems of missed detection, false detection and frequent ID switching in the tracking process of small and dense targets, and significantly improving the counting accuracy and the reliability of yield estimation.

This invention relates to the field of electrical measurement technology, and in particular to a method, system, electronic device, and medium for measuring the DC resistance of cable sheaths. The method includes: defining a sequentially connected induced current acquisition section, a measured resistance section, and a compensation injection section; extracting the frequency, phase, and amplitude reference parameters of the power frequency induced voltage signal; performing closed-loop dynamic adjustment based on a proportional-integral-differential algorithm until the residual power frequency interference voltage drops below a preset threshold; injecting a preset constant DC current into the measured resistance section and simultaneously acquiring the DC voltage drop signal across the measured resistance section; and calculating the DC resistance value of the measured resistance section based on the four-terminal test method. This method solves the technical problem of insufficient measurement accuracy of the DC resistance of cable sheaths in complex field environments with uninterrupted power supply and strong power frequency induced current, achieving high-precision, non-destructive, and portable measurement under such conditions.

The application discloses a parameter setting method and system of PSS based on complex domain vector synthesis, and belongs to the technical field of power system automation and control. The method comprises the following steps: obtaining an open-loop frequency response, and extracting a phase lag characteristic curve of a channel; based on a complex domain vector synthesis model and the phase lag characteristic curve, a complex response calculation formula is constructed, a total open-loop phase is defined, a parameter vector is set and a parameter feasible region is determined, a target compensation phase is set, a weighted comprehensive phase error target function is defined, and a boundary value is set; the weighted comprehensive phase error target function is taken as a fitness function, parameter space searching is performed, and a parameter vector that minimizes the fitness function is solved; the parameter searching process of the global optimization algorithm is optimized; the optimal parameter set is verified in multiple dimensions and vector synthesis visual analysis is performed, and parameter setting of a power system stabilizer is completed. The application has correct principles, relatively high efficiency and accuracy in processing, and high engineering applicability.

This invention provides a continuous dismantling control system and robot suitable for complex field environments, belonging to the field of robotics technology. The control system includes: a cutting task acquisition module, a cutting trajectory planning module, a movement trajectory planning module, a work point determination module, and a dismantling control module. The cutting task acquisition module acquires cutting tasks; the cutting trajectory planning module analyzes the cutting tasks and determines the cutting trajectory; the movement trajectory planning module plans the movement trajectory based on the cutting trajectory and pre-configured robot parameters; the work point determination module analyzes each point on the movement trajectory and determines the work point; and the dismantling control module determines and executes the cutting control parameters corresponding to the work point. This invention enables operation in a stable and safe location after analyzing complex field environments, providing an equipment foundation for the rapid and efficient recovery of decommissioned blades.

The application discloses a kind of peanut precision seeding regulation systems and methods, it is related to agricultural machinery technical field.The system includes seeding execution state acquisition module, seed flow state perception module, ground running state identification module, data pre-processing module, seeding state discrimination module, seeding state prediction module, collaborative control module and dynamic sampling module;The method includes parameter acquisition, two-stage filtering pre-processing, state discrimination, fusion model prediction, dynamic weight collaborative control and adaptive sampling mode switching.Through multidimensional parameter coupling acquisition, filtering noise reduction, fusion prediction and dynamic regulation, the problem of existing technology regulation lag, single dimension is solved, the precision control of peanut seeding amount and rhythm is realized, and the stability and adaptability of seeding under complex field environment are improved.

This application relates to the field of seismic measurement technology, and provides an apparatus and method for measuring cross-fault motion in experimental earthquakes. The apparatus includes a metal rod, an elastic compression spring installed at the first end of the metal rod, and a rod axial force gauge installed on the metal rod. The method involves installing the apparatus on a fault model and calculating fault motion. Based on the tensile stiffness K1 of the apparatus for measuring cross-fault motion in experimental earthquakes and the data recorded by the rod axial force gauge, the axial displacement of the metal rod, the shear displacement in the fault shear direction, and the real-time velocity and acceleration of the fault motion at time t are calculated. The apparatus for measuring cross-fault motion in experimental earthquakes of this application has a simple structure, is easy to manufacture, and can capture vibration information during fault instability without the need for complex field equipment deployment. It is not easily affected by the environment, and the test results are accurate. The method can accurately describe the instability process of experimental earthquakes.

The application relates to the technical field of sound field reconstruction, and particularly discloses a complex sound field reconstruction method and system based on a physically constrained diffusion model, which comprises the following steps: adopting a complex sound pressure form to represent a source plane complex sound field as an original clean sample of a diffusion model; gradually injecting Gaussian noise into the source plane complex sound field to construct a forward diffusion process, and training a conditional denoising network to predict corresponding noise components under the condition of a given diffusion time step and a target plane amplitude; using the predicted noise components of the conditional denoising network to back-propagate a clean sample estimation corresponding to the current time step, reconstructing the clean sample estimation into a complex field form to obtain a source plane complex sound field estimation; introducing an angular spectrum method as a display acoustic propagation model, inputting the source plane complex sound field estimation into the acoustic propagation model for propagation calculation to obtain a reconstructed complex sound field of the target plane. The application realizes stable prediction of a source plane complex amplitude hologram by modeling a conditional distribution in a high-dimensional continuous complex field space.

The application provides a complex field characteristic optimization method and system based on stray suppression and aberration compensation, comprising: designing a bias-fed parabolic body structure, setting a feed antenna, a beam complex lens, designing an aberration compensation lens group, and performing edge processing on the bias-fed parabolic body; coating a wave-absorbing material, calculating a set static zone field amplitude and phase fluctuation; if the calculated static zone field amplitude and phase fluctuation result does not conform to a preset standard, performing Fourier transform on a phase distribution curve to obtain angular position information, and performing wave-absorbing material processing on corresponding phase distribution abnormal distribution points; if the calculation result conforms to the preset standard, performing volume and weight optimization; adding a turntable, performing wave-absorbing processing, and performing static zone field characteristic calculation; if the calculation result conforms to the preset standard, completing stray suppression processing. The application performs calculation and analysis on a radio frequency field on the basis that an infrared target design meets imaging quality, thereby avoiding the problem of poor infrared imaging quality caused by an existing parabolic metal mesh.

The invention discloses a high slope surface deformation monitoring method based on edge AI and related equipment, and relates to the technical field of civil engineering safety monitoring and geological disaster early warning, and the method comprises the steps: collecting a specific target image in real time, processing the specific target image based on a preset lightweight AI model, obtaining displacement data corresponding to a specific target, and calculating the deformation of the specific target according to the displacement data. And sending the displacement data to a rear-end monitoring platform, so that the rear-end monitoring platform performs real-time monitoring on the slope surface deformation of the high slope based on the displacement data to obtain a monitoring result of the slope surface deformation of the high slope. High slope surface deformation monitoring is completed based on cooperation of the rear-end monitoring platform and the front-end acquisition unit, the front-end acquisition unit only needs to upload displacement data instead of image data, the data transmission amount is greatly reduced, meanwhile, the front-end acquisition unit processes the acquired data in real time through marginalization design, the data processing efficiency is improved, and then the data processing efficiency is improved. In a complex field environment, high-efficiency and high-precision deformation monitoring is carried out.

The application relates to a field boundary recognition method and device based on agricultural machinery trajectories, equipment and a medium. The method comprises the following steps: obtaining dimensional information contained in a plurality of agricultural machinery trajectory points; obtaining a plurality of work path line segments based on the dimensional information; rotating the work path line segments to a work coordinate system according to a work main heading angle, and extracting a one-dimensional point column formed by the work path line segments in the direction of a target coordinate axis of the work coordinate system; performing autocorrelation analysis on a density distribution histogram constructed by the one-dimensional point column to determine an equivalent width estimation value; clustering the work path line segments based on the work path line segments and the equivalent width estimation value to obtain a plurality of work field region points; constructing a triangulation for each work field region point, eliminating triangles with a circumscribed circle radius greater than a preset threshold, and forming a target field boundary by using the remaining triangles. The entire process of the application is fully automated, the field boundary can be accurately fitted without manual intervention, and the application is suitable for complex field contours with convex and concave shapes.

The application discloses a wheat ear detection method based on an improved YOLOv8s, which is suitable for improving wheat ear detection problems in a complex field environment, so as to realize rapid identification and effective counting of wheat ears. First, the application replaces a C2f module in a Backbone network by using a FasterBiNetBlock, which can reduce model parameters, reduce the number of calculation redundancies and memory accesses, and refine features of the model, so that more attention is paid to the wheat ears during detection, and interference of background factors such as wheat stems and wheat leaves is inhibited. Secondly, a new network structure EQFPN is proposed to strengthen a Neck network, so that the model has the characteristics of being friendly to quantization and hardware. Finally, a triplet attention module is introduced to capture cross-dimension dependency and wheat ear features, and meanwhile, problems such as false detection and missed detection during wheat ear detection are reduced.

The invention discloses a crop seedling growth center positioning method and system based on a leaf skeleton. The method comprises the following steps: performing leaf segmentation on a crop image and generating a binary image of a leaf mask; a mask skeleton is extracted, an undirected graph is constructed, a blade main path is determined, and a blade center fitting curve is obtained through parameterization and B spline fitting; extending the curve outwards based on the minimum circumcircle to form an extension line section; obtaining a single plant area through closed operation, and calculating a geometric centroid as a centroid clue; and extracting a convergent point of the extension line section as a convergent clue, performing adaptive selection between the two clues in combination with a stability index, and finally outputting a crop center point coordinate. The system comprises an image acquisition device, a positioning processing device and a visualization device. The method can still realize robust positioning in a complex field environment, and is suitable for precise agricultural operations such as weeding, thinning and the like.

The utility model relates to teaching appliance technical field discloses a magnetic deflection trajectory teaching protractor, include: protractor body, any one end of protractor body both ends still is provided with the protruding block, the protruding block inboard still is provided with fixed point subassembly, the fixed point subassembly center with protractor body edge place is tangent, when fixed, protractor body can rotate along the axis of fixed point subassembly to show particle magnetic deflection trajectory, the teaching protractor of this application can quickly delineate the trajectory of particle magnetic deflection, and the operation is simple relying on the rotating process of rotating protractor, since having fixed point, will not happen removal, rotates more accurate, and uses rotation to handle the trajectory problem of charged particle movement in complex field, need not draw multiple circles in the same figure, let the drawing be clearer.

Provided are a data encoding and decoding method and apparatus, relating to the technical field of computers. The method comprises: acquiring data to be encoded, and determining, on the basis of the data to be encoded and a first matrix, a check bit of the data to be encoded. The first matrix comprises N non-overlapping sub-matrices, wherein the N sub-matrices comprise a first sub-matrix and a second sub-matrix, a u-th row element of the second sub-matrix is a multiple value of a u-th row element of the first sub-matrix, or a v-th column element of the second sub-matrix is a multiple value of a v-th column element of the first sub-matrix, the multiple is an element in a complex field or a finite field, and u and v are positive integers. This allows for computations of identical matrices to be reused in an encoding and decoding process, thereby significantly reducing multiplication and XOR operation counts, reducing decoding algorithm computational complexity, reducing computational overhead, and improving overall encoding and decoding computational efficiency.

This invention, entitled "An Automated Nucleic Acid Detection System and Method Suitable for Field Conditions," belongs to the field of nucleic acid detection technology. The technical problem it aims to solve is the significant susceptibility of existing automated nucleic acid amplification analyzers to environmental factors, poor compatibility of consumables, and unreasonable power supply strategies when used in field conditions. Furthermore, existing improvement schemes are prone to conflict with core patents of the equipment. The key technical solution involves adding a field environment adaptation and control module that communicates with an embedded host computer without altering the core structure of the detection unit. This module collects environmental parameters in real time and dynamically adjusts operating parameters, pre-stores parameter configuration schemes for typical field environments, optimizes the structure and pre-loaded reagent design of the supporting microfluidic detection chip, and adds an emergency power supply optimization unit to dynamically allocate power. Simultaneously, it designs an adapted detection method, including steps such as environmental parameter acquisition, parameter adaptation, automated detection, and result output, adapting to the detection needs of complex field environments.

This application provides a high-precision online coal quality detection device and method for thermal power plants, relating to the field of coal quality detection technology. It solves the technical problems in existing technologies, such as large sampling errors due to the natural inhomogeneity of coal, the inability of single-spectral technology to accurately detect organic and inorganic components simultaneously, and the poor generalization ability of online detection models across coal types and their difficulty in adapting to complex field environmental interference. The method includes: discretizing the coal flow on the coal conveyor belt into multiple individual coal particles, and causing the coal particles to impact a target plate at a constant velocity to generate triboluminescence; using a multi-channel photoelectric detection array to collect triboluminescence waveform data of each impact event in real time; preprocessing the triboluminescence waveform data; filtering out valid impact events based on the preprocessed triboluminescence waveform data; and extracting the spectral and temporal features of each event; inputting the extracted features into a pre-established physical model and deep learning network, and outputting the coal quality parameters of each individual coal particle.

This invention discloses an intelligent control method for the smelting and refining process of lead-bismuth alloys, comprising: A1: acquiring visual images of alkaline slag from the alkaline refining molten pool of lead-bismuth alloys, molten pool smelting temperature data, and stirring operation data, and preprocessing them respectively; A2: acquiring output features from the dust stripping branch, thermal distortion stripping branch, and color deviation stripping branch; A3: performing adaptive fusion to sequentially calculate the characteristic images of alkaline slag from the pure molten pool; A4: calculating the effective alkaline slag visual features; A5: calculating the reaction progress and the critical risk value of excessive bismuth metal burn-off; A6: calculating the reaction termination judgment flag, and terminating the smelting reaction when the reaction termination judgment flag is 1. This invention can solve the problems of traditional methods being difficult to adapt to complex fields of view with strong interference, unable to accurately identify the reaction progress and the critical point of burn-off, and thus difficult to balance the impurity removal effect and the control of bismuth metal oxidation burn-off.

A method for rice panicle detection based on YOLOv8-EMS and fine-grained classification includes the following steps: First, high-quality side-view images of rice panicles are acquired to construct a dataset containing multiple single-season rice varieties. Second, a detection model YOLOv8-EMS is constructed based on YOLOv8. In the detection stage, a multi-scale edge enhancement module MSEIE, a deformable attention mechanism DATTN, and a lightweight shared convolutional detection head Detect_LSCD are introduced to improve the localization ability of dense, occluded, and small-scale panicles. In the classification stage, the detection results are input into NTS-Net for fine-grained classification, utilizing its self-supervised region selection mechanism to focus on discriminative features such as glume arrangement and awn length. Finally, the fine-grained classification results output by NTS-Net are fed back to the detection module to refine the category labels of the original detection boxes, thereby generating a final output that combines accurate location information with high-confidence category judgment. This invention solves the problem of rice panicle recognition under side-view conditions and improves the overall performance of rice panicle recognition in complex field environments.

This invention provides an image-understanding-based method for diagnosing crop diseases, belonging to the field of intelligent agricultural detection technology. This image-understanding-based method acquires crop leaf images and field environmental parameters through a multi-source data acquisition module. Adaptive preprocessing eliminates complex backgrounds and noise interference. A CNN-Transformer hybrid architecture fuses frequency-space features and environmental modal information. A semi-supervised meta-learning strategy is combined to achieve disease identification and severity classification in small-sample scenarios. Finally, a lightweight optimized model is deployed on mobile terminals. Experimental results show that this invention achieves high average recognition accuracy in diagnosing 23 types of diseases in six crops, including corn, rice, and peppers. The model size is only 8.7MB, and the inference latency is low. Compared with existing technologies, it significantly improves adaptability to complex field scenarios, accuracy in small-sample diagnosis, and mobile deployment efficiency, effectively meeting the real-time diagnostic needs of precision agriculture.