143results about How to "High engineering application value" patented technology

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

This application discloses a method and apparatus for constructing a multi-scale adaptive gating MambaPlus network, belonging to the field of artificial intelligence and machine learning technology. The method includes: initializing the network configuration and constructing the basic structure; preprocessing the input data to generate a standard dataset; mapping the input data to the hidden space via an input mapping layer, and extracting backbone features from the Mamba backbone; constructing at least two parallel scale branches in the hidden space to obtain a multi-scale feature set; inputting the backbone features and multi-scale features into an adaptive gating module, dynamically allocating weights and adaptively fusing them through a hierarchical gating mechanism to generate fused features; further enhancing the features through cross-scale attention and feedforward enhancement, and then superimposing the residuals to generate the final discriminative features; finally, completing category prediction and model training evaluation. This application, while retaining the advantages of Mamba's long-range dependency modeling, addresses the problems of insufficient utilization of multi-scale information, poor adaptive feature fusion, and low robustness in complex scenarios.

The invention discloses a pipeline defect detection method and system, and belongs to the technical field of pipeline defect detection. The pipeline defect detection method comprises the following steps: acquiring a preprocessed magnetic flux leakage signal and an ultrasonic guided wave signal; performing feature extraction on the signal to obtain a first feature subjected to multi-scale enhancement processing and a second feature subjected to convolution and attention mechanism processing; fusing by adopting a strategy including mutual information calculation and adaptive weight to obtain cross-modal fusion features; and performing multi-task detection and correction based on the fusion features to obtain a detection result indicating the type, the position and the size of the defect. According to the method, the limitation of single-mode detection can be overcome, and the accuracy of defect type identification, the positioning precision and the size quantification reliability are remarkably improved through dynamic weight distribution and physical constraint verification.

The invention relates to the technical field of three-dimensional point cloud data processing, and provides a subsidence-water-area-oriented point cloud stripe noise LTCF elimination method and system, and the method comprises the steps: obtaining point cloud data, preprocessing, filtering, and optimizing point cloud output. The filtering processing comprises the following steps: local covariance feature extraction: aiming at each target point in the preliminarily optimized point cloud obtained by preprocessing, constructing a k-nearest neighbor domain, and calculating local covariance contribution values of each target point in X, Y and Z spatial directions; compared with the prior art, the method has the following beneficial effects that the industrial pain point of a subsidence water area scene is solved, a three-direction covariance joint judgment mechanism is innovatively introduced, exclusive recognition logic is designed for specific low-elevation strip noise only containing XY-direction covariance in the subsidence water area, the lamellar strip noise which cannot be recognized completely by a traditional algorithm can be accurately captured, and the recognition accuracy of the subsidence water area scene is improved. And the problem of eliminating the point cloud core noise of the subsidence water area is solved fundamentally.

The application discloses a kind of prediction methods of high-strength steel wire high temperature creep behavior of rust, belong to bridge cable safety assessment technical field.The method is first obtained by test the material intrinsic material parameters of rust high-strength steel wire test piece and un-rust high-strength steel wire test piece;Then three-dimensional scanning quantization corrosion morphology is used, the maximum pit depth of rust high-strength steel wire surface and the pit root radius of pit bottom are extracted, and then the stress concentration coefficient and the effective bearing area decay function after net section loss are calculated, and the corrosion damage index is introduced;On this basis, the creep strain rate prediction model considering local stress concentration, section loss and material damage is established.The prediction method of the application realizes the quantitative prediction from pit micro-morphology to macro creep behavior, and has great engineering application value for evaluating the safety and residual life of in-service bridge cable.

This invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency feature mapping. The method includes: at the transmitting end, adaptively generating a fractional-order linear frequency-modulated waveform with a specific time-frequency shear slope based on the Doppler state of the underwater acoustic channel, achieving physical-layer focusing of transmitted energy; at the receiving end, constructing a hybrid observation model containing wide-block sparse channel components and narrow-block sparse burst noise components, and using a dual-channel variational Bayesian algorithm to jointly iteratively infer the posterior probability distribution of environmental burst noise in the fractional-order domain; finally, recovering the original signal through soft-threshold interference cancellation and fractional-order channel equalization. This invention effectively solves the communication failure problem caused by high-dynamic Doppler diffusion and marine biological impulse noise interference in underwater acoustics by actively mapping the waveform and using heterogeneous sparse joint inference at the receiving end, significantly improving transmission reliability in harsh underwater acoustic environments.

This invention relates to an IMU-assisted online GNSS anomaly detection and reporting method and apparatus. The method involves acquiring GNSS observation data and IMU measurement data and aligning them in time; generating a predicted motion state of the receiver at the current observation epoch based on the IMU measurement data; calculating the predicted GNSS observation value of at least one satellite at the current observation epoch based on the predicted motion state and satellite ephemeris information; constructing a residual sequence based on the predicted GNSS observation value and the corresponding GNSS observation value in the GNSS observation data, performing sliding time window statistics, and determining anomalies based on the statistical results; when an anomaly is determined, generating a time index and a spatial index corresponding to the anomaly, forming a spatial-level interference event, and reporting it. This invention effectively reduces the false alarm rate and missed alarm rate in scenarios involving obstruction, multipath interference, and other interference, exhibits higher sensitivity to observation anomalies caused by moving interference, effectively lowers the implementation threshold at the receiving end, and possesses high engineering application value.

A discrete manufacturing pipeline quality prediction method based on mechanism and data joint driving belongs to the field of intelligent manufacturing and industrial artificial intelligence, and the method comprises the following steps: first, data acquisition and preprocessing under the industrial scene of the discrete manufacturing pipeline; second, state space deduction and single variable nonlinear analysis of the sparse time sequence characteristics of the discrete manufacturing pipeline; third, mechanism joint training and real-time closed-loop intervention of the discrete manufacturing pipeline quality prediction model. The present application significantly improves the physical consistency of the discrete manufacturing pipeline quality prediction, realizes continuous inference of the hidden state under irregular sampling gaps, and enhances the safety decision-making ability and engineering application value of the automatic control of the discrete manufacturing pipeline.

The application discloses a double PMSM compound error weight adaptive prediction position synchronous control method, comprising the following steps: S1, according to the mathematical modeling of the double permanent magnet synchronous motor system, an incremental continuous control set model prediction control model is established, and a multi-step prediction is carried out to obtain an output matrix; S2, based on the multi-step prediction output matrix, average tracking error and semi-modal synchronous error are constructed, and a value function is established; S3, according to the design of a Lyapunov function, a weight adaptive update law is designed by using a gradient descent method; S4, an event triggering mechanism is designed for the weight adaptive update law, if an event triggering condition is met, the weight adaptive update is carried out, otherwise, the weight of the last moment is used; S5, the optimal control variable increment is obtained by minimizing through the weight calculation after the update, whether the constraint condition is met is judged, if the constraint condition is met, the optimal control variable increment is directly output, otherwise, the optimal control variable increment is output after being subjected to geometric constraint.

This invention discloses an electromechanical braking system and its control method based on heterogeneous motor redundancy, relating to the field of brake-by-wire technology for new energy vehicles. The system includes the following steps: During vehicle braking, a permanent magnet synchronous motor (PMSM) acts as the main drive source, outputting braking torque, while a switched reluctance motor (SRM) operates in standby or low-power auxiliary mode, forming a heterogeneous motor redundant drive structure. This invention achieves complementary failure modes and reduces the risk of common-mode failures by employing a heterogeneous redundancy structure of a PMSM and a SRM; simultaneously, it optimizes system economy by utilizing the low cost and low standby energy consumption characteristics of the SRM. A deep learning health prediction model is introduced to achieve early fault prediction and proactive switching, and a rapid, seamless switching mechanism within 20ms ensures braking continuity and system safety.

A modular intelligent pipe support, belonging to the field of pipeline vibration control, includes a support body, sensors, a vibration signal acquisition, analysis, and alarm module, and a dynamic vibration absorption module. This invention is geared towards practical engineering applications. While retaining the boundary constraints and vibration control functions of existing pipe supports, it adds a modular device that integrates dynamic vibration absorption, energy harvesting, vibration monitoring, anomaly alarms, and wireless communication functions. This solves the problems of existing pipe supports having limited functionality, being unable to effectively handle pipeline spectrum vibrations, and requiring significant manpower and resources for routine safety inspections. Its core technology lies in utilizing a cantilever beam-mass block structure to provide targeted dynamic vibration absorption reaction force, effectively controlling pipeline spectrum vibrations; using piezoelectric materials to convert the mechanical energy generated during the cantilever beam vibration process into electrical energy, thereby powering the online monitoring module; and using sensors to collect the support vibration signals in real time, thus realizing the monitoring and analysis of support vibrations.

The invention relates to the technical field of vehicle control, and discloses a vehicle starting control method and device, a vehicle and a medium, and the method comprises the following steps: in response to starting operation of the vehicle in an automatic parking state, adjusting parking release control parameters according to current road surface information and vehicle parameters; and releasing the automatic parking state according to the adjusted parking release control parameter. The parking release control parameters are dynamically adjusted according to real-time road surface information and vehicle parameters through a dynamic and self-adaptive parameter adjusting mechanism, the limitation of traditional fixed calibration is eliminated, different road conditions and vehicle states can be adapted, the vehicle starting quality in the automatic parking state is remarkably improved, and the vehicle starting speed is increased. And fixed parameters under the automatic parking function can be continuously optimized, so that the method adapts to individual manufacturing differences of different vehicles, starting abnormities such as slope sliding, dragging and vehicle channeling of the vehicles can be effectively avoided, the safety and smoothness of vehicle starting are improved, and meanwhile the user driving experience and the vehicle intelligent control level are greatly improved.

The application discloses a three-shaft order sensing multi-objective optimization VMD (Vibration Mode Decomposition) gas turbine vibration signal decoupling method and belongs to the technical field of gas turbine state monitoring and signal processing. The application aims at the problem that the traditional VMD method appears serious mode aliasing due to the multi-shaft order deep coupling of three-shaft gas turbine vibration signals and strong wideband combustion noise. First, the order prior information of three-shaft rotors is fused, a three-shaft order decoupling entropy index is proposed, and a four-dimensional multi-objective optimization system containing average envelope entropy, three-shaft order decoupling entropy, combustion noise suppression index and instantaneous frequency stability is constructed. Then, the NSGA-III algorithm is used for multi-objective global optimization, and the objective decision of optimal parameters is completed through the entropy weight TOPSIS method. Finally, the adaptive decomposition and order decoupling of vibration signals are realized based on the optimal parameters. The application can completely solve the problem of mode aliasing caused by multi-shaft order coupling, and greatly improves the decoupling effect and feature extraction accuracy of three-shaft gas turbine vibration signals.

The application relates to a hybrid AC / DC microgrid based on a reconfigurable modular multilevel converter and an operation method and a storage medium thereof. The microgrid comprises a modular multilevel converter (MMC), the MMC comprises a plurality of phase legs, each phase leg comprises an upper arm and a lower arm, each arm is connected with a plurality of sub-modules in series, each sub-module comprises a semiconductor switching device, a capacitor energy storage unit and a controllable bypass switch structure; the MMC is connected with an AC bus and a DC bus respectively to realize bidirectional power conversion. The system comprises a plurality of feeder branches which are connected with the bus through power switching devices. Distributed edge controllers are arranged at the sub-modules or feeder interfaces to collect voltage and current parameters and output isolation or bypass signals in an abnormal condition; meanwhile, hierarchical reconfiguration controllers are arranged to perform structure reconfiguration and power path redistribution. The application realizes fault rapid isolation and reconfiguration cooperation through hierarchical control, maintains the voltage stability of the key load bus, and realizes the effect of improving the system reliability and power supply continuity.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

This invention discloses a deep learning-based method for VOCs pollution feature analysis and source tracing. The method includes acquiring online monitoring time-series data of VOCs and synchronous meteorological parameters of the monitoring area, constructing a multidimensional input tensor after preprocessing, building a deep neural network model containing a spatiotemporal feature extraction module and a source apportionment regression module, extracting nonlinear chemical coupling features and temporal evolution features between VOCs components using a one-dimensional convolutional neural network and a bidirectional long short-term memory network, and weighting and fusing them using meteorological parameters, and finally decoding and outputting the source component spectrum and temporal contribution rate that conform to physical constraints through the source apportionment regression module to generate a pollution feature map. This invention effectively solves the problem that traditional receptor models struggle to handle nonlinear chemical transformations and complex meteorological couplings, improves the accuracy and physical interpretability of VOCs pollution source tracing, and has online update capabilities to adapt to environmental changes.

The invention discloses a double-beam synchronous following anti-reflection device for laser forging printing, which is characterized in that the specific wavelength combination of 1064nm powder bed cladding laser and 532nm green ultrafast strengthening laser is adopted to impact a fusion layer, and a special reflecting coating with the wavelength of 532nm is coated on a scanning galvanometer mechanism; therefore, the coating can selectively transmit laser of 1064nm and efficiently reflect laser of 532nm at the same time by utilizing the obvious wavelength difference between the two; on the basis, diffuse reflection of green ultrafast laser can be selectively isolated, so that damage to the laser caused by return light is fundamentally avoided, stable work of equipment in a long-time and high-power following mode is ensured, and therefore, the device is very suitable for large-scale application and popularization in the technical field of laser forging printing.

This invention provides a method for detecting rotating debris in coal flow based on physical fuzzy modeling and scene-customized attention. The method includes: acquiring a clear coal flow image and then labeling non-coal debris targets in the image; establishing a dataset; constructing a debris detection network; constructing a physics-driven motion fuzzy mapping model; using the motion fuzzy mapping model to dynamically blur the clear coal flow image, generating training samples simulating motion fuzziness phenomena in a conveyor belt; training and optimizing the debris detection network; inputting a real-time coal flow image into the optimized network to obtain candidate detection results for non-coal debris targets; post-processing the candidate detection results and outputting the final filtered detection results. This invention can improve the stability and accuracy of debris detection under complex conditions of high-speed coal flow, reduce the influence of motion fuzziness and background interference, and provide technical support for the safety monitoring and automatic sorting of coal mine conveying systems.

This invention belongs to the field of medical image processing technology, specifically a system for aortic motion feature extraction and disease risk prediction based on medical image analysis. The system includes modules for acquiring multi-temporal image sequences and clinical data, dynamic segmentation of aortic anatomical structures, construction of a dynamic relative coordinate system, trajectory preprocessing and standardization, trajectory segmentation analysis based on physiological phases, multidimensional dynamic feature extraction, and risk stratification prediction model construction. This invention integrates multidimensional motion features with clinical indicators to construct a risk model based on machine learning or statistical regression, outputting a predicted value for the individual's cardiovascular disease risk. This invention can reduce global interference caused by respiratory and cardiac translation, improve the biological specificity and inter-individual comparability of features, achieve fully automated processing, and enhance the risk prediction ability for cardiovascular events such as heart failure, atrial fibrillation, and aortic disease, showing promising application prospects.

This invention discloses a fault location method for a traction power supply system feeder circuit breaker in a 1-to-2 mode, relating to the field of electrified railway traction power supply technology. By converting the cross-connecting current between the AT substation and the section substation into the up and down feeder currents of the traction substation, the first fault interval from the traction substation to the AT substation can also be located using the cross-connecting current ratio method. This method also considers the differences between Type I and Type II substations. Using this method can improve the fault location accuracy of the first fault interval, controlling the location error within 250 meters. This ensures that maintenance personnel can quickly locate the fault point, promptly handle the fault, and restore normal line operation when a fault occurs, which is of great significance for ensuring the safe, stable, and efficient operation of the railway transportation system.

The application provides an improved sparse learning out-of-grid direction-of-arrival estimation method based on variational Bayesian. The method is characterized in that: the original data received by a hydrophone array is preprocessed, a real value transformation is used to convert a vectorized covariance matrix signal in a complex number field to a real number field, and the idea of variational sparse Bayesian learning and grid evolution is combined to make the grid evolve from an initial uniform grid to a non-uniform grid adaptively in an iteration process. The evolution process includes grid updating and grid fission. The evolved grid points are gradually close to the real source position through the alternately iterative grid updating process and grid fission process. Compared with the traditional compressed sensing method, the method has higher DOA estimation accuracy, reduces the operation complexity, optimizes the operation efficiency, improves the resolution capacity of the source, and has higher application value in actual engineering, especially in the case of few snapshots and low signal-to-noise ratio.

The utility model discloses an exhaust vane type silencer structure of an air-cooled heat pump unit, which is mounted above an exhaust fan component of the air-cooled heat pump unit and comprises an upper silencing insert, a lower plenum chamber and a flow deflector component arranged in the lower plenum chamber. The flow deflector assembly comprises flow deflectors, fixing ribs and fixing columns, the flow deflectors are at least used for adjusting flow field distribution in the silencer, and the fixing ribs are connected with the flow deflectors and the fixing columns. The flow deflector is used for adjusting the flow field of airflow entering the silencer insertion piece part, so that the flow field in the silencer is more uniform, the situation that airflow regeneration noise is increased due to the fact that the local wind speed is too high is avoided, and finally the actual noise reduction performance of the silencer is improved.

This invention relates to a method for modeling the capillary evolution of humidity-related adhesion forces under micrometer-scale probes. This method establishes a physical relationship between ambient relative humidity and Kelvin radius, and calculates the Kelvin radius in conjunction with the initial volume of the liquid bridge, thereby constructing a parameterized model of capillary forces that characterizes the change in liquid bridge volume with ambient humidity. The influence of the liquid film on the solid surface is considered during model construction, enabling the model to more realistically reflect the formation and evolution of liquid bridges in actual micro / nano interfaces. As the two surfaces gradually move away, the relationship between the curvature of the liquid bridge interface and the Kelvin radius is established under the condition that the liquid bridge volume remains constant, thus achieving a continuous description of the evolution of capillary and adhesion forces during the separation process between the micrometer-scale probe and the sample interface. Furthermore, experimental measurements and model verification of micrometer-scale interface forces are conducted using a self-developed fiber Bragg grating probe.

The invention specifically relates to a BIM-based hydraulic engineering design earthwork volume calculation method, and relates to the technical field of hydraulic engineering design, and the method comprises the steps: generating a cross-regional scheduling instruction through a high layer, and carrying out the coevolution process of intra-regional precise optimization and cost feedback iteration through a bottom layer until the overall total cost is converged, and obtaining an optimal cross-regional scheduling and intra-regional deployment scheme. According to the method, qualitative engineering constraints such as soil matching, construction time sequence and transport distance economy are converted into quantifiable transport network graph theory rules such as effective edge filtering, time sequence verification and flow limitation, and it is ensured that the measurement and calculation process strictly conforms to the actual construction logic; the risk that a deployment scheme is not feasible due to the fact that constraint consideration is not comprehensive in a traditional method is avoided from the source, and double improvement of measurement and calculation precision and construction adaptability is achieved.

The invention relates to the technical field of electricity market electricity price prediction, and discloses a spot clearing electricity price prediction system fusing new energy output fluctuation characteristics, and the system comprises a fluctuation characteristic extraction module which carries out the variational mode decomposition of a new energy output prediction sequence, and generates fluctuation impact momentum and duration; the climbing scarcity calculation module is used for calculating a reference equivalent climbing scarcity in combination with the polymerization residual climbing capability and the fluctuation impact momentum; the hysteresis state correction module is used for correcting and generating a scarcity degree state index by using a state attenuation parameter based on fluctuation duration; the supply curve reconstruction module is used for generating a dynamic effective supply curve by carrying out linear deformation on the reference residual supply curve by utilizing the physical capacity cut-off quantity mapped by the index and a quotation slope correction coefficient; and the clearing electricity price solving module is used for outputting a predicted value based on the curve. The new energy fluctuation impact and hysteresis effect are effectively quantified, and the prediction precision is improved.

This invention provides a fire extinguishing performance testing device for fire protection systems used in energy storage systems, belonging to the field of fire extinguishing performance testing technology. It includes a basic characteristic parameter acquisition module, a test scheme intelligent generation module, a runaway test condition simulation module, a multi-dimensional parameter acquisition and monitoring module, and a fire extinguishing performance evaluation and analysis module. The invention constructs an individual characteristic parameter set for the tested energy storage system through the basic characteristic parameter acquisition module, enabling the test scheme intelligent generation module to generate targeted dynamic time-series test schemes. This achieves precise matching between the test scheme and the actual operating conditions of the energy storage system, as well as dynamic simulation of the entire thermal runaway process. Through the multi-dimensional parameter acquisition and monitoring module and the fire extinguishing performance evaluation and analysis module, it achieves a comprehensive evaluation of fire extinguishing efficiency, cooling performance, and decontamination effectiveness across three dimensions. Based on the distance to the evaluation nodes, it automatically outputs the optimal fire-fighting strategy, improving the intelligence level and engineering application value of the testing device.

This invention belongs to the field of microstructure evolution prediction technology for hot working of nickel-based superalloys, and discloses a quantitative prediction method for the dynamic recrystallization form and degree of GH4698 alloy under hot deformation. The method includes the following steps: obtaining the hot deformation parameters of the alloy, including the hot deformation temperature and equivalent strain; calculating the temperature-coupled critical strain using the hot deformation parameters, including the non-recrystallization critical strain, the DDRX initial critical strain, and the DDRX dominant critical strain; determining the dynamic recrystallization form based on the equivalent strain and the temperature-coupled critical strain; and calculating the dynamic recrystallization volume fraction based on the dynamic recrystallization form, thereby achieving a quantitative prediction of the dynamic recrystallization form and degree of the alloy under hot deformation. This invention solves the problem of large prediction errors in recrystallization behavior in existing technologies.

The turbofan engine Reynolds number correction method based on the equal load coefficient of the application, including S1: determining the test point of the non-self-model area and the self-model area; S2: taking the test point of the non-self-model area as a correction point, and taking the test point of the self-model area as a reference point; S3: obtaining the component Reynolds number correction factor under different Reynolds number index conditions according to the equal load coefficient correction, and establishing a Reynolds number correction relationship curve; S4: applying the component Reynolds number correction relationship curve to the corresponding component performance of the baseline overall performance calculation model to obtain an engine overall performance calculation model with Reynolds number correction. Avoid the situation that the calculation deviation of the whole machine model is large due to the influence of Reynolds number at high altitude and low Mach number.

The application discloses a kind of based on hypergraph enhanced YOLOv11 frame weed detection method and device, method includes: based on YOLOv11 baseline network, reconfigures local-global collaborative main network, obtains first optimization network reconfiguration neck network is hypergraph enhanced semantic neck, to establish high-order space topological feature fusion mechanism, obtains second optimization network;Second optimization network is used as the final enhanced detection model, and based on the enhanced detection model, weeds in field are detected;Using the constructed data set and multiple public data sets, the enhanced detection model is comprehensively experimentally verified;The enhanced detection model after verification is used for weed detection.The device includes: processor and memory.The present application solves the problem that the weed detection model in the prior art has low detection accuracy due to insufficient global context perception, weak geometric deformation representation capability and missing high-order topological relationship in complex farmland scenes.