136 results about "Complex field" patented technology

The invention provides an electromagnetic field prediction method and device and electronic equipment, and relates to the technical field of electromagnetic field solving. The method comprises the following steps: acquiring historical electromagnetic original data in a field-line coupling scene, preprocessing the historical electromagnetic original data, inputting the preprocessed historical electromagnetic original data into an LSTM-PINN model, and outputting a physical field quantity mapping result; wherein the physical field quantity comprises an electric field component and a magnetic field component; setting a weighted loss function, and performing optimization training on the LSTM-PINN model based on a physical field quantity mapping result and an error of a real physical field quantity corresponding to historical electromagnetic original data to obtain a trained electromagnetic field prediction model; wherein the weighted loss function comprises a data loss function, a physical residual loss function, an initial condition loss function and a boundary condition loss function; and inputting real-time electromagnetic original data into the trained electromagnetic field prediction model to obtain a spatio-temporal distribution electromagnetic field prediction result. The method can effectively extract the spatial distribution features and the time sequence features at the same time, and is suitable for a complex field-line coupling problem.

The invention relates to the technical field of computer vision, and particularly discloses a weed root recognition method and device based on attitude estimation, and the method comprises the steps: obtaining field image data containing a weed plant, detecting a leaf key point through a pre-trained leaf attitude estimation model, fitting a leaf vein extension line, calculating an intersection point cluster, and carrying out the recognition of a weed root. And screening a high-density candidate point set through density clustering analysis, calculating a weighted center coordinate as a weed root positioning result according to density weight, and transmitting the weighted center coordinate to laser weeding equipment to execute precise weeding operation. According to the method, accurate weed root identification is realized, the positioning accuracy is higher, the robustness to a complex field environment is higher, the real-time performance can be improved by utilizing model lightweight, efficient deployment is realized, and the data labeling cost is reduced.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on a double-domain fusion expansion model. The method comprises the following steps: acquiring an MRI image, preprocessing the MRI image to obtain a preprocessed image and a corresponding compressed sensing measurement value, and constructing a data set; training, testing and verifying the double-domain fusion expansion model by using the data set; and outputting a reconstructed image from the MRI image after mask sampling by using a double-domain fusion expansion model. According to the double-domain fusion expansion model, a complex convolutional neural network and a K-space attention mechanism are introduced for complex value data processing and double-domain information fusion in compressed sensing MRI reconstruction. The complex value data is processed through the complex network, the amplitude and phase information of the complex field is fully utilized, the detail recovery capability and the noise suppression effect of the model are enhanced, and particularly, excellent robustness is shown at a low sampling rate. The method is excellent in performance in complex MRI data reconstruction, and has high reconstruction precision and generalization ability.

The invention discloses a displacement auxiliary measuring mechanism and a reservoir dam monitoring and early warning system thereof, and belongs to the technical field of dam monitoring. The reservoir dam monitoring and early warning system comprises a data acquisition module, an attitude calibration module, a data fusion judgment module, a risk grading analysis module and a remote early warning trigger module. The distance measurement precision under the complex field condition is remarkably improved, the current mechanical direction and the reference direction are fused, a correction vector pointing to a target reflection point is generated, the direction drift error is effectively eliminated, and the distance measurement accuracy is improved by constructing a double-path model of main path fusion and auxiliary path comparison. According to the method, non-structural anomalies caused by sensor body interference or environmental disturbance are eliminated, data offset is corrected in real time, a stable trend curve is output, when a main path has a drift or error trend, the main path is automatically guided to converge to an auxiliary path, and the abnormal data recognition and restoration capability is enhanced.

The invention provides an equipment maintenance support question and answer method based on a fusion RAG big language model. The method comprises the following steps: obtaining question information and a corresponding text tag; labels of the corresponding sub-layers are obtained from a local knowledge base through the text labels; retrieving and aggregating K most relevant entities according to the labels of the sub-layers, and generating prompt words; obtaining retrieval information by using a large language model LLM and generating an intermediate reply; and generating a detailed reply in combination with label information of a higher-layer graph in the local knowledge base, and carrying out iterative optimization on the detailed reply until a final reply is generated. According to the method, the offline knowledge base and the large language model are deployed, and the information retrieval capability and the generation capability of the large language model are combined by utilizing the RAG technology, so that the performance of the question-answering system in the complex field is improved, and the question-answering function for equipment maintenance support in the professional field is realized.

The invention discloses a multi-platform real-time field rice ear detection and counting method based on a visual converter, which is suitable for various acquisition platforms from a portable mobile terminal to an unmanned aerial vehicle terminal and the like, and meets the requirements of small-range accurate identification and large-range high-throughput monitoring. Aiming at the characteristics of multiple scales, multiple varieties, phenotype difference, time sequence change, complex field environment and the like in a data set, a rice spike detection counting network comprising a feature enhancement module, an efficient cross-space feature extraction network, a lightweight bidirectional feature fusion module and a number estimation and detection head module is constructed. The average precision mean value (mAP50) of the model reaches 0.8906 and is improved by 11.5% compared with that of an original model, the parameter quantity is reduced by 29%, and the calculated quantity (GFLOPs) is reduced by 48%. The method can effectively process rice ear images of different scales and growth periods, adapts to morphological change, overcomes interference such as illumination, phenotypic difference and shielding, and provides technical support for rice yield evaluation and field rice ear quality analysis.

The invention provides an unmanned agricultural machine obstacle avoidance operation path optimization method and device in a farmland scene, and relates to the field of automatic driving and path planning, and the method comprises the steps: finding a collision risk strip through detecting whether there is an intersection point between an unmanned agricultural machine operation strip and an obstacle expansion range in a farmland; based on key parameters such as the machine tool size and the minimum turning radius of the agricultural machine, the minimum influence range of the obstacle repulsive force effect is restrained, and it is ensured that the obstacle avoiding path meets the actual obstacle avoiding requirements of the agricultural machine and the mounted machine tool of the agricultural machine in space; local obstacle avoidance path planning is carried out based on an artificial potential field method, the distance relation between sampling points on an operation strip and the center of an obstacle substance is introduced, deflection angle adjustment is carried out on the repulsive force direction in combination with the obstacle influence radius, and therefore potential field distribution is dynamically optimized. According to the method, the operation requirement, the path smoothness, the path continuity and the operation efficiency can be considered, and the application value of the unmanned agricultural machine in the complex environment in the field is improved.

The invention discloses a multi-agent cooperative task method and device based on a hierarchical task mechanism, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the hierarchical abstraction based on a task space of a target domain, obtaining a plurality of hierarchies, configuring a corresponding agent and a tool for each hierarchies, constructing a state management mechanism to manage intermediate data among a plurality of hierarchies; in response to target information input by a user, decomposing and planning a target task by using a plurality of mutually associated hierarchies to obtain a directed acyclic graph of the target task; calling and executing the subtasks corresponding to the nodes from bottom to top based on the directed acyclic graph so as to complete the target task; and integrating a completion result of the target task and then rendering the result to a user interface. The problem that an existing intelligent agent system is difficult to meet the requirements of complex fields with highly structured and hierarchical features is solved. A more efficient, accurate and robust scheme can be provided for complex tasks with highly structured and hierarchical features.

The invention discloses a pseudo-ginseng flower maturity detection method based on an improved YOLOv11 model, belongs to the field of agricultural intelligent detection and deep learning application, and relates to deep learning algorithm improvement with YOLOv11 as a baseline model, mainly to optimize and enhance three aspects of backbone network replacement, neck network optimization and loss function improvement. On the premise of slightly increasing the complexity of the model, the detection speed is kept to adapt to edge equipment, and the real-time operation requirement of the picking robot is met. A core detection method is provided for intelligent picking of the panax notoginseng flowers, the detection technology blank of small-target and easily-confused growth stages in a complex field environment is filled, and the method has engineering application value for promoting automatic upgrading of the panax notoginseng industry.

The invention discloses an airborne laser radar collimation axis error correction method based on an oval scanning track, belongs to the technical field of error correction, is used for collimation axis error correction, and comprises the following steps: correcting points of the oval scanning track and correcting a normal vector of a reflector; performing forward fixed-point scanning on an object to be measured by using the laser radar to obtain a forward oval scanning track, performing backward fixed-point scanning to obtain a backward oval scanning track, and obtaining points of the corrected oval scanning track; and calculating a scaling factor, and correcting the normal vector of the reflector to complete collimation axis error correction. The method does not need external auxiliary equipment, does not depend on ground control points or regular ground feature information, is suitable for complex application scenes without deployment and control, without overlapping areas and the like, and improves the adaptability, correction precision and engineering practicability of the system; the practicability in a field complex environment is remarkably improved, and the method is suitable for single-route flight in an uncontrolled area; the method has a trajectory scale distortion correction capability, and enhances the spatial consistency of sounding point clouds.

The invention discloses a method of a terminal strip field alignment intelligent system based on augmented reality. The system collects a terminal strip image / video in real time through a mobile device, identifies a terminal number and a wire sleeve label by using an AI algorithm of an edge server, and compares the terminal number and the wire sleeve label with a structured CAD drawing in real time. Based on a comparison result, the system visually superposes a matching state on a real-time picture through an augmented reality technology, and supports intelligent interaction for manual correction. According to the method, high-real-time and high-accuracy alignment can be realized in a network-free environment, the efficiency is remarkably improved, and the method is suitable for a complex field environment.

The invention discloses a YOLOv11n-based modeling method for an insulator defect detection model of an unmanned aerial vehicle polling high-voltage transmission line, and the method comprises the following steps: 1, collecting an insulator defect image through an unmanned aerial vehicle, and carrying out the preprocessing of the insulator defect image, and forming a data set; step 2, constructing an insulator defect detection model based on the improved YOLOv11n; step 3, training the insulator defect detection model based on the improved YOLOv11n constructed in the step 2 to obtain an optimal model; and 4, performing precision verification on the optimal model constructed in the step 3 by adopting a verification set to obtain an insulator defect detection model meeting a precision requirement. According to the method, by deeply optimizing a model structure and introducing an innovative module, high-precision identification of insulator defects is realized on the premise of ensuring low computing resource consumption. The method can effectively adapt to efficient inspection tasks in various complex field environments, and the detection performance and the practical value at the edge equipment end are remarkably improved.

The invention belongs to the technical field of agricultural machinery and intelligent control, and particularly relates to an intelligent precise weeding robot based on multispectral recognition. The robot aims at solving the problems that in the prior art, due to visible light morphological recognition, the forms of crops and weeds are similar, and the recognition precision is low and the robustness is poor in the complex field environment. The robot comprises an autonomous mobile platform and a sensing operation assembly, the sensing operation assembly integrates a multi-mode sensing unit, a data fusion and decision core and a high-energy thermal ablation module, and the multi-mode sensing unit provides eight narrow-band pulse light with specific wavelengths through an active light source illumination module. Realizing synchronous acquisition of spectrum and space information by combining multispectral imaging and three-dimensional profile measurement; the data fusion and decision core performs registration, segmentation and classification on multi-source data to generate weed target coordinates; and the high-energy thermal ablation module implements 1470-nanometer laser accurate ablation based on the coordinates, and verifies the operation effect in real time through coaxial infrared thermal imaging. By means of the technical scheme, high-confidence-coefficient distinguishing and millimeter-level lossless precise weeding of crops and weeds in the complex farmland environment can be achieved.

The invention discloses a method and a system for improving the operation precision of a rotary tillage seeder. The method comprises the following steps: acquiring working environment and machine state information in real time through a soil hardness sensor, a humidity sensor, a gyroscope and a GNSS (Global Navigation Satellite System) module; the rotary tillage depth and the sowing depth are detected, and operation deviation is calculated based on a multi-source data fusion algorithm; according to the deviation result, the rotating speed of a rotary tillage cutter shaft, the downward pressure of the seeder and the posture of the machine are dynamically adjusted through a self-adaptive control strategy, and the seeding depth and the rotary tillage depth are consistent; the operation track is optimized in combination with GNSS navigation, and the row spacing and plant spacing precision is ensured; and after the operation is finished, evaluating the uniformity and consistency of the operation. The system comprises a data acquisition module, a data fusion module, a control decision module, an execution mechanism and an operation evaluation module. According to the method and the system, the operation precision and the stability of the rotary tillage seeder can be improved in a complex field environment.

The invention discloses a tilling depth detection method and system based on three-dimensional data, and relates to the technical field of precision agriculture, and the method comprises the steps: building nonlinear mapping based on a corrected attitude angle and a lifting arm angle, updating the nonlinear mapping through extended Kalman filtering, obtaining a high-precision real-time attitude angle, and obtaining a tilling depth detection result; and constructing an initial tilling depth model based on the high-precision real-time attitude angle, and fusing the target tilling depth and the LiDAR depth to obtain a target tilling depth value. By fusing data of multiple sensors such as LiDAR, RGB-D and IMU, adopting complementary filtering and an RANSAC algorithm to correct an attitude angle and improve attitude estimation precision, a point cloud density and signal-to-noise ratio adaptive weight mechanism is introduced, fusion of target tilling depth and visual depth is optimized, robustness and accuracy of tilling depth detection under a complex field condition are improved, and the method is suitable for large-scale popularization and application. The problem that a traditional method is insufficient in adaptability to mechanical dynamic changes and terrain slopes is effectively solved.

The invention relates to a method for realizing a dynamic complex data type field by an online table, which comprises the following steps of: 1, defining the configuration of the online table by metadata Metadata, and defining the name and the field information of the data table by the metadata; 2, the table metadata and the field metadata of the table metadata are stored in a metadata collection metadata resource set in a database; 3, a table data storage module achieves efficient management through the database, and each table corresponds to one collection in the database and is used for storing all records in the table; and 4, on the basis of field metadata configuration, in combination with the incidence relation table, ensuring the accuracy and the real-time performance of the data through a dynamic calculation mechanism. The problem that the flexibility of cross-table data association, dynamic data reference and aggregation calculation is insufficient when dynamic complex data type fields are processed in an online table is solved. Complex field types can be easily defined, query efficiency is improved, storage space use is optimized, and system consistency is improved.

The invention discloses an acquisition device based on rock mass structural surface roughness, and relates to the technical field of rock mass structural surface roughness measurement, the acquisition device comprises a frame type base, a probe assembly, a laser scanning module and a real-time data processing unit, the lower surface of the frame type base is symmetrically provided with liftable supports, and the probe assembly is arranged on the lower surface of the frame type base; the frame-type base is of a square frame-type structure, the bottom of the frame-type base is open, the frame-type base is provided with a height-adjustable supporting column, the center of the top face of the frame-type base is provided with a linear sliding rail, the sliding rail is provided with a slidable measuring table top, and the measuring table top is driven by a motor to move. Vibration interference caused by irregularity of a device or a rock surface is suppressed, and laser scanning and a multi-point synchronous acquisition technology are combined, so that the information space continuity of the structural surface is improved; real-time dynamic measurement in a complex field environment is achieved, efficiency and data authenticity are improved, and the method has popularization value.

A fault diagnosis method for the RF front-end circuit of a MIMO system includes using a Synchronous Enhancement Extracting Transform (SEET) to pre-process the acquired fault signal to extract fault feature, creating a fault identification model fused by a complex field based asymmetric convolutional neural network and a complex field based multi-head attention module to assign fault feature weights, extract key feature and identify fault status. The SEET can extract fault feature components, and calculate its real field feature and imaginary field feature to obtain a real field two-dimensional matrix i and an imaginary field two-dimensional matrix q, thus an enhanced time-frequency feature is obtained. The fault identification model is used for a transform from a complex field feature space to a high dimensional space and realizing the assignment of fault feature weights, key features extraction and fault status identification.

The invention discloses a pseudo-ginseng flower maturity detection method based on an improved RT-DETR model, and belongs to the field of agricultural intelligent detection and deep learning application, the pseudo-ginseng flower maturity detection method is an improvement of a deep learning algorithm taking RT-DETR as a baseline model, and mainly performs optimization and enhancement on three aspects of backbone network replacement, neck network optimization and AIFI module partial replacement. On the premise of ensuring the detection speed, the lightweight detection model with lower complexity and higher detection precision is successfully realized. The model provides a core detection method for intelligent picking of panax notoginseng flowers, fills the blank of small-target and easily-confused growth stage detection technologies in a complex field environment, has important engineering application value, and promotes automatic upgrading of the panax notoginseng industry.

The invention discloses a crop insect pest detection method and system based on improved ConvNeXt, and relates to the technical field of crop insect pest detection, and the method comprises the steps: constructing an EDA-ConvNeXt model, training the EDA-ConvNeXt model to obtain a crop insect pest detection model, according to the method, the detection precision is improved through the EDA-ConvNeXt model, the multi-scale features are captured by the backbone network, the boundary information is enhanced by the edge enhancement branch, the dynamic feature extraction branch adapts to the morphological change, and the key features are highlighted by the multi-branch fusion. The classification layer ensures the recognition reliability, and finally higher accuracy is achieved in a complex field scene.

The invention discloses a multi-mode joint information source channel coding system for satellite-ground semantic communication. The multi-mode joint information source channel coding system comprises a joint information source channel coder and a decoder, the encoder is used for sequentially performing feature extraction, feature fusion, data cutting and phase modulation operation on input multi-modal data to generate a transmitting signal, and transmitting the transmitting signal to a ground base station through a channel; and the decoder is used for sequentially performing real and virtual part separation, data expansion, feature extraction and modal data recovery operation on the received signal so as to reconstruct multi-modal data. According to the system, public semantic information among multi-modal data can be mined, the time-varying low-signal-to-noise-ratio channel characteristics of a satellite downlink are combined, the information bottleneck theory highly related to data reconstruction quality is used as guidance to optimize a coding space, complex field transmission signals with the high compression ratio are generated in a self-adaptive mode, and the multi-modal multi-domain transmission signals are obtained. The reconstruction quality of the multi-modal data can be ensured, and the bandwidth and the energy consumption overhead of satellite communication can be remarkably reduced.

The invention discloses a self-leveling anti-seismic field emergency intelligent operating bed, and relates to the technical field of operating beds. Comprising a supporting table and a supporting box fixedly installed at the top of the supporting table, an operating bed body is installed on the top wall of the supporting table, and according to the self-leveling anti-seismic field emergency intelligent operating bed, through arrangement of a leveling assembly, when the operating bed needs to be arranged in the field, firstly, switching of a stable supporting state of a moving state can be rapidly completed; under the self-weight action of the whole device, the pressing seat and the cone are pushed to be inserted into the ground to form primary support, and intelligent self-leveling is conducted on the supporting table through the horizontal control mechanism according to the horizontal state of the whole supporting table, so that the supporting table is rapidly kept in the horizontal supporting state, and the inclination or shaking condition is prevented; and during intelligent leveling, adjustment and adaptation can be carried out according to each fulcrum, especially in the complex field environment, stable leveling can be completed on the uneven ground, and better environmental adaptability is achieved.

The present invention provides a TIN-DDM terrain complexity measurement method based on rolling ball transformation, belonging to the technical fields of seabed terrain information mining and analysis and TIN-DDM multi-scale expression technology. The present invention introduces the change in sampling point depth during the rolling ball transformation process into the construction process of the TIN-DDM sampling point TCI calculation model. The present invention clarifies the correlation between the depth change of the TIN-DDM sampling point and its TCI during the rolling ball transformation process, analyzes the selection principle of the optimal rolling ball radius, constructs and optimizes the sampling point TCI calculation model, and finally achieves an effective measurement of TIN-DDM terrain complexity by interpolating the complex field with the TCI. This solves the problem that the TCI calculation models established by the current TIN-DDM terrain complexity measurement methods are unable to effectively reflect the differences in morphological change characteristics between different terrain units by comparing the TCI values of the sampling points.

The invention discloses a rice ear grain identification method and system based on a deep learning neural network, and the method comprises the steps: firstly carrying out the precise mask marking and systematic preprocessing of a rice ear image, and enhancing the quality of input data; secondly, a self-attention mechanism is introduced into the MaskR-CNN model, the long-distance dependency relationship between any positions in a feature map is effectively captured, the understanding ability of the model for the overall structure of the rice panicles is enhanced, and particularly the recognition effect on the rice panicles which are shielded or incomplete in shape is improved; furthermore, a multi-point regression strategy is fused, regression weights are dynamically distributed according to different IoU thresholds, and regression results of a plurality of high-quality candidate boxes are comprehensively utilized for fine correction, so that the accuracy and robustness of rice spike positioning are remarkably improved. Therefore, the method can effectively cope with interference caused by a complex field environment, reduces false detection and missing detection while ensuring a high recall rate, realizes more accurate and stable automatic statistics of rice ears, and provides reliable data support for subsequent agricultural decision making.

The invention discloses a multi-focus composite Fresnel phase diagram rapid generation and power equalization method and system for one-to-many free space laser communication. According to the method, aiming at a scene in which a plurality of user targets are located at different directional angles and different propagation distances, a focusing + directional single-beam phase is constructed for each target on the basis of analyzing a Fresnel zone lens phase and a linear tilt phase, and the phase is taken after complex field superposition is performed according to a preset power weight, so that the target target is obtained. A composite phase diagram for a phase-type spatial light modulator (SLM) is directly obtained. The power weight is preset according to a link budget or a task demand so as to realize on-demand distribution and equalization of multi-beam output power; and meanwhile, fixed phase bias can be introduced to each beam to suppress inter-beam interference fringes. According to the method, frame-by-frame iterative solution is not needed, the calculation complexity is low, and high-frame-rate and low-delay online updating is easy to realize.

The invention relates to the technical field of agricultural instruments, in particular to a plant height measuring device based on visual processing, which comprises a lifting guide column for guiding measuring equipment, a supporting assembly is arranged between the measuring equipment and the lifting guide column, and a marking mechanism matched with the measuring equipment for use is arranged on the supporting assembly. A driving mechanism for lifting the measuring equipment is arranged on the lifting guide column; the measuring device is of a folding structure. Non-contact measurement is adopted, mechanical clamping damage to plants is avoided through airflow gathering, the device is particularly suitable for fragile crops, meanwhile, the position of the spray pipe is adjustable, the device can adapt to different plant types and complex field environments, in the measurement process, measurement and marking are synchronized through mechanical linkage, the data management efficiency is improved, and the device is suitable for popularization and application. After a user starts a driving motor, the measuring equipment automatically ascends and descends, centralizes plants, calculates the plant height and triggers the marking mechanism to spray after the plants are in place, and manual intervention is not needed in the whole process.

The invention discloses a strawberry growth period identification and detection method based on improved RT-DETR. The method comprises the following steps: constructing a data set based on collected image samples of different growth periods of strawberries; annotating the image samples in the data set to generate annotation files corresponding to the image samples; dividing the image samples in the data set and the annotation files corresponding to the image samples into a training set, a test set and a verification set; carrying out growth period identification detection on the strawberry photo through an improved RT-DETR algorithm module; according to the method, the common problems of difficulty in small target detection and fuzzy stage characteristics in strawberry growth period identification tasks are effectively solved; the method not only enhances the combined modeling capability of shallow details and deep semantic features, but also improves the expression and interaction of cross-scale and remote dependent features, and effectively improves the recognition accuracy of the model for densely shielded and small-sized targets and similar categories in a complex field environment. And a reliable technical support is provided for crop growth monitoring in intelligent agriculture.

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 invention provides an acoustic-magnetic time difference calculation method based on signal quality self-adaption, and belongs to the technical field of cable fault detection. The method comprises the following steps: acquiring an acoustic signal and an electromagnetic signal of high-voltage cable partial discharge, and detecting the electromagnetic signal to determine a triggering moment; intercepting a sound signal segment and a background noise signal segment by taking the moment as a reference, and calculating a signal-to-noise ratio and a maximum peak-to-peak value after preprocessing; the trigger point is compared with a preset threshold value, and a trigger point positioning scheme is dynamically selected; determining an effective trigger point according to the scheme, and calculating an original acoustic-magnetic time difference; and calculating the current sound signal energy, and correcting the original time difference in combination with the reference energy to obtain a final time difference. According to the method, through an adaptive matching scheme, the accuracy and reliability of time difference calculation are improved, different signal quality scenes are adapted, and the application adaptability of a complex field environment is enhanced.

The present application relates to the technical field of image recognition, and proposes a rice ear grain detection and counting method and system in a complex field environment. The method comprises: acquiring a to-be-tested image of rice ears in an un-picked state in a complex field environment; inputting the to-be-tested image into a trained rice ear grain detection and counting model; the rice ear grain detection and counting model comprises a feature extraction and fusion module, a shielding perception module, a detection frame proposal module and a classification regression module; the feature extraction and fusion module extracts and fuses the ear grain features of the input image, and outputs an ear grain feature map and a fused feature map; the shielding perception module generates a shielding mask map according to the ear grain feature map; the detection frame proposal module generates a target detection proposal frame according to the fused feature map; the classification regression module performs target classification and detection frame regression of the ear grains according to the fused feature map, the shielding mask map and the target detection proposal frame, and outputs a detection frame and a counting result of the rice ear grains.