323 results about "Model sample" patented technology

The invention relates to a near-infrared coal quality detection method based on multi-dimensional data fusion so as to improve the coal detection precision. According to the method, a modeling sample set containing multi-dimensional data of the producing area of a coal sample, granularity characteristics, the distance between the coal sample and a probe, air humidity and environment temperature is constructed, near infrared spectrum data of the coal sample is collected, spectrum preprocessing, feature extraction and data fusion are carried out, and finally a quantitative analysis model is constructed to predict the coal quality characteristics in a high-precision mode. According to the method, a one-dimensional convolutional neural network (1d-CNN) technology is adopted, automation and intelligence of coal quality detection are realized, and the detection efficiency is improved. In addition, accurate data of moisture, ash content and volatile components in the coal sample are obtained strictly according to a national standard experimental method, and the representativeness of the sample and the reliability of the data are improved. Noise interference is effectively reduced through the preprocessing step, and scattering influence is corrected. The method provides powerful technical support for quality control, reasonable utilization of resources and the like in the coal industry.

The invention provides an intelligent prediction method and device for the bearing capacity of a pile foundation, and the method comprises the steps: forming an in-situ test sample based on an engineering geology in-situ test; the optimal geological parameters of the karst geology are obtained through inversion of the proxy model; and geological parameters of the field test under multiple working conditions are obtained, and a pile foundation field test sample library is formed. Obtaining pile foundation bearing performance parameters of various karst geologies under a pile foundation reduced scale model experiment to form a pile foundation reduced scale model sample library; carrying out analogue simulation on the optimal geological parameters of each karst geology to form a pile foundation analogue simulation sample library; constructing a pile foundation bearing capacity training sample library by the three sample libraries; training the pile foundation bearing capacity prediction model; and geological parameters of a construction site are input into the pile foundation bearing capacity prediction model, and the pile foundation model of the actual construction site is optimized and adjusted based on the obtained geological parameters. According to the method, data of four research paradigms are fused, and a more comprehensive and accurate intelligent prediction method for the bearing capacity of the pile foundation is constructed.

The invention discloses a quality evaluation method and device of a model sample, a storage medium and computer equipment. The method comprises the steps of inputting sample data into an artificial intelligence generation content detection model, and obtaining a hit probability of the sample data; matching a content evaluation system based on the attribute information of the sample data; processing the sample data based on an evaluation rule in the content evaluation system, and determining a test value of the sample data relative to at least one preset evaluation index; and based on a target weight corresponding to the hit probability and a preset evaluation index, carrying out weight calculation on the hit probability and the test value to obtain a quality score of the sample data. According to the method, data which is generated by AI but may mislead model training can be filtered, the purity and credibility of the sample data set are remarkably improved, multi-dimensional and high-precision evaluation of the training data can be achieved, different task requirements are met, and the training efficiency is improved. And the generalization ability of the model based on sample data training and the adaptive ability to unknown data are improved.

The invention provides a diesel engine misfire fault diagnosis method based on a GRU and a PINN. The diesel engine misfire fault diagnosis method comprises the following steps of diesel engine multi-source data acquisition, data preprocessing and model sample generation; designing a gating circulation unit, introducing a physical constraint embedding module, constructing a misfire fault diagnosis model, and fusing the misfire fault diagnosis model with a long-short term memory model and a physical neural network; performing joint training and optimization on the misfire fault diagnosis model in the step S2 by using the samples in the step S1; and deploying a misfire fault diagnosis model and performing real-time diagnosis. The method has the beneficial effects that the time sequence data-physical model dual-drive diagnosis of the misfire fault of the diesel engine is realized; the accuracy of misfire diagnosis is improved; and the false alarm rate of complex working conditions is effectively reduced.

The invention relates to a large-scale model experiment method for visually measuring the whole stress migration process of underground engineering. The method is suitable for the geotechnical engineering field. According to the technical scheme, the method comprises the steps that protolith samples at corresponding positions of underground engineering are obtained, and mechanical properties of the protolith samples are measured; based on the mechanical properties of the protolith sample, preparing a similar material capable of reproducing the mechanical properties of the protolith sample, and pouring the model sample by using the similar material; based on an initial crustal stress field of an underground engineering field in-situ test, applying a three-way load to the model sample, and excavating the model sample; obtaining internal stress and strain information of the model sample, global strain and stress information of the model sample, fracture and energy characteristics of the model sample and fracture information of an excavation area of the model sample in the excavation process; based on internal stress and strain information, global strain and stress information, fracture and energy characteristics and fracture information of an excavation area of the model sample in the excavation process, the whole stress migration process of the excavation-affected area in the model sample is monitored.

The invention belongs to the technical field of defect detection, and relates to a PCB defect detection method based on image processing, which comprises the following steps: collecting defect-free PCB image data, and making a data set; loss functions are set for the reconstructed subnet and the segmented subnet respectively, the data set is used for training to obtain the trained reconstructed subnet and the segmented subnet, and the trained reconstructed subnet adopts a diffusion model to carry out noise removal on to-be-detected image data subjected to preprocessing and data enhancement step by step according to time steps to obtain a reconstructed image. And decomposing the reconstructed image into a low-frequency component and a high-frequency component through discrete wavelet transform, inputting the low-frequency component into the trained segmentation subnet, comparing the low-frequency component with the to-be-detected image data, and distinguishing a defect part. According to the method, the number of used samples is far smaller than that of other target detection model samples, discrete wavelet transform is utilized, high-frequency components are extracted for back diffusion denoising, the reasoning speed is increased, and the segmentation and defect detection precision is improved through the extracted low-frequency components.

The invention discloses a personality evaluation-oriented big language model enhanced text mapping processing method and system. The method comprises the following steps: S1, constructing a correlation model sample database; constructing a BERT model to obtain text features of user social text data, and then performing average pooling processing to obtain an initial vector; s2, firstly performing social text data splicing processing, and performing extraction embedding processing and average pooling processing through a Llama3 model to obtain a user integration vector; s3, constructing a fusion model containing three full connection layers and an attention mechanism, and carrying out addition fusion to obtain a final user vector; and S4, performing personality label interpretation and coding by using a GPT-4 model and a BERT model, and performing contrast learning model training by using a target label contrast learning model. According to the method, the model sample database is used for carrying out model processing and relation training of the BERT model, the Llama3 model, the fusion model, the target label comparison learning model and the like, and the learning precision and efficiency of the user vector and personality label relation model are remarkably improved.

The invention relates to a three-dimensional model flaw detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining three-dimensional model sample data, carrying out the data preprocessing and data division of the three-dimensional model sample data, and generating a training data set, a verification data set and a test data set; initializing the YOLO model to obtain an initial flaw detection model; converting data in the training data set into two-dimensional images of multiple angles to obtain a target two-dimensional image, and performing flaw detection on the target two-dimensional image through the initial flaw detection model to obtain a flaw detection result; calculating model index parameters and model loss, and adjusting the model parameters; performing iterative training on the adjusted initial flaw detection model through the target two-dimensional image to obtain a target flaw detection model; and detecting the to-be-detected three-dimensional model data through the target flaw detection model to obtain a target flaw detection result. According to the method, the three-dimensional model flaw detection efficiency and accuracy are improved.

The invention discloses a real estate evaluation method based on Bayesian optimization and machine learning, and the method comprises the steps: collecting data, and constructing a real estate evaluation index system; the collected data are preprocessed; using feature engineering to convert each feature variable in the preprocessed data into data capable of being trained, and generating a modeling sample set; carrying out normalization on the data, and carrying out dimensionless standardization processing on the original data; building a real estate evaluation model based on an XGBoost algorithm in machine learning; a Bayesian optimization algorithm is used to optimize hyper-parameters of the real estate evaluation model, and an optimal hyper-parameter combination is found; training the optimized real estate evaluation model, and testing; and according to the basic information of the house, outputting an evaluation price of the house through a house property evaluation model. According to the method, the value of the house can be evaluated scientifically and accurately through the combination of technical means such as building of the house property evaluation index system, building of the house property evaluation model and Bayesian optimization of hyper-parameters.

The invention discloses a cable defect detection method of a binocular intelligent inspection robot, and relates to the field of image processing, and the method comprises the steps: firstly obtaining the state information of a cable, then carrying out the preprocessing and stereo matching of the image information of the cable, and then constructing a target detection and segmentation model; the target detection and segmentation model is adopted to detect and segment cable defects, and model sample data is updated in real time in an incremental learning mode; fusing the cable state information acquired by the infrared camera, the thermal imager and the wireless sensor network by using a multi-sensor data information fusion model; the cable is maintained and managed based on a detection result; according to the invention, the detection precision and accuracy can be improved, cables of different types and specifications can be handled, the adaptability and stability are ensured, and the real-time performance and accuracy of detection can be improved.

The invention discloses a visual recognition algorithm polymorphic model microtechnology based on edge cloud and an application system thereof, and belongs to the technical field of computer vision, the visual recognition algorithm polymorphic model microtechnology comprises a cloud end and an edge end, the cloud end comprises a polymorphic model management module and a model optimization module, the edge end comprises a preprocessing module, and the polymorphic model management module is connected with the model optimization module. Comprising n related visual identification models, an optimal model is automatically matched for a current task through an intelligent decision-making mechanism based on calculation precision, complexity and load of different visual identification models, and the model optimization module collects visual identification model samples in use. According to the visual identification algorithm polymorphic model microtechnology based on the edge cloud and the application system thereof, model management, model screening, model optimization and resource optimization can be realized, the response speed and the resource utilization efficiency of a visual identification system are remarkably improved, and the visual identification algorithm polymorphic model microtechnology is suitable for various application scenes with high requirements on real-time performance, accuracy and safety.

The invention discloses a photovoltaic power station operation and maintenance management system and method based on big data, and relates to the field of photovoltaic power generation, and the method comprises the steps: collecting the operation data of a photovoltaic module, carrying out the preprocessing of the data through an edge computing device, and storing the data to a cloud big data platform; based on the stored photovoltaic module operation data, establishing a photovoltaic power station power generation behavior basic model by using a self-supervised learning method; performing traceability analysis on abnormal power generation behaviors in the distributed account book in combination with historical data, and triggering an operation and maintenance task by using an intelligent contract; after the operation and maintenance task is executed, the processed abnormal power generation behavior is fed back to the self-supervised learning model sample library, and the model is dynamically adjusted; based on the operation and maintenance task execution content, an operation and maintenance report is generated and uploaded to the block chain; according to the method, the operation and maintenance task is automatically triggered through the intelligent contract, full-process automatic management from anomaly detection to task issuing is realized, and the operation and maintenance efficiency and the operation stability of the photovoltaic power station are improved.

The invention belongs to the field of analytical chemistry and mathematical modeling, and particularly relates to a method for predicting and identifying whether soy sauce contains yeast extracts, which comprises the following steps: 1) taking different types of soy sauce as modeling samples; 2) carrying out decolorization pretreatment on the modeling sample; (3) carrying out spectrum scanning on the modeling sample by adopting a Raman spectrum technology to obtain original Raman spectrum information; 4) preprocessing the Raman spectrum data obtained in the step 3); 5) sampling for multiple times to establish a sparse logistic regression model, and recording important wave numbers; 6) counting wavenumber importance; 7) selecting an important wave number; 8) using TabPFN to establish a prediction model; (9) predicting and judging whether the yeast extract is added into the soy sauce sample or not by utilizing the established prediction model; compared with the prior art, the method can quickly and accurately identify whether the soy sauce such as light soy sauce and dark soy sauce contains the yeast extract or not, and is simple and quick in identification process, short in analysis time, high in accuracy and high in practicability.

The invention discloses a landing typhoon residual vortex detection method based on YOLOv5, and the method comprises the steps: employing ERA5 data and landing typhoon optimal path data, screening a convolutional neural network YOLOv5 model, carrying out the screening of initial data based on a potential height field, taking the screened data as a model sample set, building a typhoon residual vortex detection model, and achieving the detection of weak vortexes. In the life stage after the landing typhoon is weakened into tropical low-voltage shutdown, the existence and development conditions of the landing typhoon residual vortex are accurately detected and monitored, accurate reference is provided for meteorological early warning and disaster management, and the accuracy rate of a test set is as high as 70.08%.

The invention relates to the field of gallium nitride high electron mobility transistor (GaN HEMT) compact modeling, and provides a p-GaN gate HEMT model establishment method based on a physical guided neural network (PGNN) in order to solve the problem of p-GaN gate HEMT compact model development, and the method comprises the following steps: constructing a p-GaN gate HEMT transistor model by using a TCAD simulation tool, generating a model sample, and extracting simulation data; constructing a DerivNet model and a loss function based on a physical guidance neural network; and finally, performing model training by using the simulation data to obtain the p-GaN gate HEMT model based on the physical guidance neural network.

The invention relates to the technical field of data management, in particular to a pearlescent material production data management system and method based on data integration analysis. The method comprises the following steps: obtaining a process original data set of a pearlescent material production line, and carrying out standardization processing to obtain a standardized process data set; obtaining a quality detection data set of the pearlescent material; performing time interval division on the standardized process data set and the quality detection data set, and performing batch number marking to obtain a process-quality batch data pair; and based on the process-quality batch data pair, designing a modeling sample data set, and constructing a quality process coupling model. According to the invention, the intelligent, data-driven and high-quality stable control of the pearlescent material production process is comprehensively realized by constructing a closed-loop system integrating data integration, modeling prediction and regulation and control optimization.

The invention discloses a test method for simulating rock hydraulic fracturing in a true triaxial stress state, and relates to the technical field of hydraulic fracturing, the test method comprises the following steps: S1, a sample preparation stage: processing a rock block into a standard square rock sample, and processing a plurality of hydraulic fracturing holes according to a test target size; s2, a sample mounting stage; s3, an equipment inspection stage; s4, a pre-load applying stage; s5, a hydraulic pressure applying stage of the hydraulic fracturing model: injecting hydraulic fracturing fluid with different flow rates into the hydraulic fracturing holes at different flow rates by utilizing a constant-flow constant-pressure pump through the hydraulic fracturing hydraulic pressure applying pipeline, and recording data and closing the constant-flow constant-pressure pump after the square rock sample is damaged; s6, an unloading stage; and S7, ending the test. According to the invention, a single hydraulic fracturing hole or a plurality of hydraulic fracturing holes are processed on a square rock sample to simulate single well and multi-well model samples, and different hydraulic pressurization tests are realized, so that the qualitative analysis of the reservoir rock crack evolution law under the true triaxial stress condition is completed.

The invention discloses a generative model reasoning deployment method and device based on AI questions and answers, and the method comprises the steps: constructing the basic configuration of an AI questions and answers system, and carrying out the model deployment; designing a database and storing model metadata, a model example code, a GPU (Graphics Processing Unit) configuration guide and a Dockerfile script template; receiving a user reasoning service request, generating a deployment task ID and recording task information; performing model existence check, and if the model is missing, automatically downloading model metadata according to a stored Git warehouse address; extracting corresponding configuration data and example code details from the database, and dynamically filling a script template with the configuration data and example code details to generate a Dockerfile script; executing a mirror image starting script constructed in the container, running a mirror image and returning a service address; according to the invention, the efficiency and convenience of model service deployment are improved, the threshold of AI technology application is reduced, the resource configuration is optimized, and the user experience is enhanced.

The invention relates to a continuous flow chemical reaction yield soft measurement method and a continuous flow chemical reaction yield soft measurement system. According to the method, real-time online prediction of the continuous flow chemical reaction product yield is realized through a deep learning technology. Firstly, soft measurement modeling parameters are selected, and after data acquisition, a soft measurement modeling sample set is created through data alignment processing and dynamic time window feature construction. The soft measurement model is based on a multi-head attention mechanism enhanced LSTM, a multi-scale feature extraction and frequency domain analysis mechanism is fused, and local dynamic features under different time resolutions are extracted through a multi-scale convolution kernel; key time steps in a long sequence and a global dependency relationship of the key time steps are effectively captured through a multi-head attention mechanism enhanced LSTM module; and the periodic fluctuation characteristics in the time series data are extracted and identified through the frequency domain characteristics. The invention provides an economical, efficient, real-time and reliable intelligent solution for yield monitoring in the continuous flow chemical reaction process.

The invention discloses an image editing method and device and a storage medium. The method comprises the steps that diffusion processing is conducted on an original image through a diffusion model; determining the total number of noise reduction iterations by using a first model trained in advance; and in each noise reduction processing process, sampling a network module of the noise prediction network in the diffusion model by using a pre-trained second model, and carrying out noise reduction processing by using the network module selected by sampling to obtain an edited target image. By applying the scheme of the embodiment of the invention, the first model determines the total number of iterations of noise reduction, and the second model samples the network module of the noise prediction network, so that the proper number of iterations and the proper network module can be adaptively selected for different images, and the noise reduction efficiency is improved on the basis of ensuring the image quality. The complexity of a long iteration process and a network structure is greatly avoided, and the generation efficiency of image editing is effectively improved.

The present disclosure provides a method and an apparatus for evaluating the quality of model samples, a storage medium and a computer device. The method includes: inputting sample data into an AI-generated content detection model to obtain a hit probability of the sample data; matching a content evaluation system based on the attribute information of the sample data; processing the sample data based on the evaluation rule in the content evaluation system to determine a test value of the sample data relative to at least one preset evaluation index; and performing a weighted calculation on the hit probability and the test value based on a target weight corresponding to the hit probability and the preset evaluation criterion, to obtain a quality score of the sample data. This method is capable of filtering data that may mislead model training, and also realizing a high-precision evaluation of the training data.

The present invention relates to the technical field of industrial device detection, and discloses a device damage intelligent monitoring method and system based on mechanism and working condition big data. The monitoring method comprises: first, setting various damage modes of a device, constructing a mechanism model sample of each damage mode on the basis of a technical standard and an engineering case, and constructing a working condition big data sample of each damage mode on the basis of field data; then, using the mechanism model sample and the working condition big data sample as data sets for model training so as to respectively construct a mechanism model and a working condition model; and finally, inputting real-time text data to be predicted and real-time working condition data to be predicted into the mechanism model and the working condition model, respectively, calculating the possibility that the real-time text data belongs to various damage modes and the possibility that the real-time working condition data belongs to various damage modes, and generating a device damage monitoring result on the basis of the two possibilities. The present invention effectively improves the utilization rate of real-time information during damage monitoring of a pressure-bearing device, so that the monitoring result is more accurate.

The invention relates to the technical field of artificial intelligence and hydraulic engineering, in particular to a gate dam safety monitoring multi-element collaborative prediction and early warning method based on deep learning. The method comprises the following steps: synchronously collecting monitoring data such as displacement, osmotic pressure and temperature of a gate dam through an automatic safety monitoring facility, carrying out collaborative correction processing on the monitoring data, and converting the monitoring data into a standardized sequence sample; intercepting the standardized sequence sample into a source sequence and a target sequence, constructing a model sample library, and dividing the model sample library into a training set and a verification set; training a pre-constructed gate dam safety monitoring multi-element collaborative prediction and early warning model by using the training set, evaluating the precision of the model by using the verification set, and automatically calibrating model parameters; and utilizing the determined model to execute prediction so as to prompt early warning. According to the method, the problems of lack of fusion of global association and local dependence, short model prediction period and the like of traditional safety monitoring single-station modeling can be solved, the limitation of single-element threshold alarm is broken through, and the multi-element cooperative monitoring and early warning precision and efficiency are improved.

The invention discloses a medical image clustering method based on correlation entropy and an adaptive bipartite graph, and the method comprises the steps: collecting image data of medical imaging equipment, and forming a sample data matrix; selecting anchor point samples from the sample data matrix, and establishing an original sample low-dimensional representation-anchor point low-dimensional representation graph for capturing a local geometric structure of an embedded space; introducing an orthogonal basis matrix; carrying out joint modeling on the sample reconstruction error, the structure retentivity, the adaptive graph constraint and the discriminative features by adopting a joint objective function; iteratively updating the joint objective function; and inputting the finally obtained low-dimensional representation of the original sample as an embedded representation into a clustering device of K-means, and completing final clustering label distribution. According to the method, more stable, efficient and interpretable unsupervised clustering is realized, the clustering precision and generalization ability are improved, and the method is particularly suitable for medical image data analysis tasks of high-dimensional and complex structures.

The invention discloses a sample screening method and system for an agent model and a microwave device, and the method comprises the steps: sampling and obtaining an initial design point, screening the design point through a fuzzy clustering algorithm, and storing the design point in a database; judging whether the database meets a preset stop condition or not, if yes, outputting the optimal design, and if not, turning to the next step; using the fuzzy clustering algorithm again to simplify the database; thirdly, designing a plurality of design targets, respectively constructing a proxy model for each design target, and optimizing prediction functions obtained by the plurality of proxy models constructed in the third step by using a multi-target optimization algorithm to obtain a target solution set; performing full-wave simulation on the target solution set, judging whether corresponding design points in the target solution set meet design requirements or not, if so, outputting the target solution, and otherwise, turning to the next step; performing candidate design point selection on the target solution in the target solution set by adopting a parallel point adding criterion, performing full-wave simulation, adding a real response value into the database, and returning to the step 2, thereby reducing the design cost of the microwave device.

The invention discloses an intelligent optimization automatic control system and method for a wet-process phosphoric acid fluorine absorption process. The system comprises a data acquisition module, and the data acquisition module is in communication connection with a prediction model building module through a data transmission module; the prediction model construction module is in communication connection with the control optimization module through the data transmission module, and the control optimization module is electrically connected with the control execution module; the data acquisition module acquires data to construct a model sample data set; a fluorine absorption prediction model is constructed based on the model sample data set, and the fluosilicic acid concentration and the scaling rate are predicted through the constructed fluorine absorption prediction model; based on the prediction result and the real-time data acquired by the data acquisition module, control variable optimization is carried out through a control optimization model of multi-objective optimization, and an optimal control variable value is obtained; and the control execution module receives the optimal control variable value and calls a flow control valve to perform control and adjustment, so that the wet-process phosphoric acid fluorine absorption system reaches the optimal working condition.

The invention discloses an activity identification method based on conditional antagonism data enhancement, which comprises three parts of a conditional generative adversarial network, a kinematic feature constraint module and a prototype sample generator, a system hardware structure is designed based on an embedded heterogeneous computing platform, a main processor adopts an ARM Cortex-A72 core operation condition generative adversarial network, and the model sample generator is designed based on an embedded heterogeneous computing platform. The coprocessor adopts NPU to accelerate kinematics parameter calculation, and the sensor interface module is connected with a six-axis IMU sensor through an SPI bus to collect data of an accelerometer and a gyroscope in real time. According to the activity recognition method based on conditional antagonism data enhancement, enhancement data with diversity, authenticity and semantic accuracy can be provided for a human body activity recognition model in a scene of insufficient training data, so that the overall recognition accuracy of the model on a UCI HAR data set is improved by 14.7%, the F1 value on the aspect of few sample categories is averagely improved by 21.3%, and the recognition accuracy of the model on the UCI HAR data set is greatly improved. And an effective technical solution is provided for data scarce scenes such as medical monitoring and motion analysis.

The invention relates to the technical field of data storage, and discloses a storage management method and system based on data encryption, and the method comprises the steps: obtaining a model sample set, building a feature thermodynamic diagram based on the model sample set, determining a key feature cluster based on the feature thermodynamic diagram, carrying out the decoupling of the key feature cluster according to an auto-encoder, determining a model data set, and storing the model data set in a database. To-be-stored classification data are determined based on a multi-head self-attention model, each piece of to-be-stored classification data is layered based on analytic hierarchy process, a judgment matrix is constructed, and corresponding encryption strategies are adopted for all the to-be-stored classification data based on a data comprehensive score to determine encrypted data. The temporarily stored encrypted data is moved to the target sub-memory according to the data verification result, the backup mode of the target sub-memory is determined based on the storage occupancy rate, whether the backup mode is adjusted or not is judged based on the data retrieval condition, and the stability and reliability of data storage management are ensured.

Embodiments of the present disclosure relate to a general image prior based on Langevin diffusion. An original representation (image, 3D model, audio) is optimized to resemble a data distribution learned by a trained diffusion model. The original representation may be incomplete and is completed by the optimization process. In an embodiment, the diffusion model may be trained to use an additional conditioning input, such as a text prompt. The diffusion model receives a noisy latent as input and generates a denoised output, such as an image. Generally, the diffusion model samples the learned data distribution to produce the output. The conditioning input provides additional constraint that causes the output to be “nudged” towards the learned data distribution. An example synthesis problem is to generate a panorama image that is much larger compared with images used to train the diffusion model.

The invention relates to the technical field of livestock measurement, in particular to a beef cattle body size intelligent measurement device and method, in the beef cattle body size intelligent measurement device and method, hoof joint point pixel coordinates are converted and angle differences are calculated in combination with a lexicographical sequence block reordering algorithm, identity binding codes are accurately generated, uniqueness and stability of beef cattle individual recognition are ensured, and the accuracy of beef cattle body size measurement is improved. The method effectively reduces the identification confusion risk when multiple beef cattle are measured at the same time, generates a simulation sample through local pixel block rendering and generative adversarial network, screens a sample according with a threshold according to authenticity scores, improves the sample richness, optimizes the coverage of a model training set, extracts body size fields and position coding values, and calculates correlation coefficients between fields. Low-correlation field pairs are removed, only high-correlation fields are reserved for cross combination, a modeling sample pool is constructed, the representativeness of samples and variable combination diversity are enhanced, and the fitting precision and popularization capability of a body size estimation model are improved.