95results about How to "Realize precise identification" patented technology

The invention discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention discloses an electromagnetic environment simulation method and system of a display module, a medium and a product, and relates to the field of electromagnetic variable measurement, and the method comprises the steps: receiving a test identification code inputted by a user, extracting a plurality of target scenes corresponding to the test identification code from a preset scene library, and generating a scene change sequence; determining a radio frequency interference source combination and a mechanical vibration parameter corresponding to the target scene; setting test parameters of the test time periods corresponding to the multiple target scenes, and generating a dynamic test sequence; receiving a key test scene marked by a user, and adjusting a test time period and test parameters corresponding to the key test scene to obtain a target test sequence; simulating an electromagnetic environment of the target display module, and recording simulation test parameters of the target display module; the simulation test parameters comprise display brightness, contrast and response time; and determining an electromagnetic compatibility evaluation result based on the target test sequence and the simulation test parameters. According to the invention, the accuracy of an electromagnetic compatibility test result can be improved.

The invention provides a prognosis risk prediction model construction method based on coexistence of acute myocardial infarction and atrial fibrillation and application. The method comprises the following steps: S1, acquiring historical patient clinical information of a target patient and death time after coexistence of diseases; s2, preprocessing clinical information of historical patients; s3, using an LASSO method and a ten-fold cross validation method to identify related clinical features related to death time after coexistence of diseases; s4, dividing a training set and a verification set; s5, performing training optimization on the four machine models by using the training set, evaluating the prediction performance of the models by using the verification set, and determining a Glmnet model with the optimal prediction performance; and S6, performing model interpretation on the Glmnet model by using an SHAP value, and determining an optimal prediction factor of which the model prediction contribution ranks the first three. The prognosis risk of coexistence of acute myocardial infarction and atrial fibrillation can be accurately predicted, and reference is provided for clinical diagnosis and treatment of patients with coexistence of acute myocardial infarction and atrial fibrillation.

The invention relates to a probe contact impedance real-time monitoring and compensating method and device and a storage medium. The method comprises the following steps: acquiring a first measurement data set containing a probe contact resistance value and a contact force, and inputting the first measurement data set into a double-layer deep learning neural network for training to obtain a probe contact resistance monitoring model; performing two-parameter fault detection to obtain a contact state classification result; generating a two-dimensional compensation matrix based on a contact state classification result, and performing nonlinear mapping calculation on the second measurement data set to obtain a compensation coefficient; the compensation coefficient, the original probe structure parameters and the original probe operation parameters are input into a graph attention stacking auto-encoder for parameter prediction, and target probe structure parameters and target probe operation parameters are obtained; cloud model calculation and weighting operation are carried out, a target compensation instruction is obtained, and the target compensation instruction is used for adjusting the probe position parameters and the probe pressure parameters. According to the invention, the probe testing precision of the chip is improved.

The invention relates to the technical field of cold-chain supply chains, and discloses an energy management and recovery method and system for a cold-chain supply chain. The method comprises the following steps: acquiring and preprocessing operating parameters and environmental parameters of the cold chain system to obtain multi-dimensional feature data; performing feature extraction and local density distribution calculation on the multi-dimensional feature data to obtain energy consumption mode clustering data; constructing an equipment operation knowledge graph according to the energy consumption mode clustering data; performing abnormal feature analysis on the equipment operation knowledge graph and the energy consumption mode clustering data through a hybrid neural network model to obtain equipment energy consumption abnormal feature data; generating an initial energy recovery scheme according to the equipment energy consumption abnormal feature data; the initial energy recovery scheme is applied to the cold chain system, the operation efficiency data and the load matching data are collected, online iterative optimization is carried out, the target energy management scheme is obtained, dynamic adjustment and continuous optimization of the energy recovery scheme are achieved, and the energy utilization efficiency of the cold chain system is improved.

The invention relates to a mine smoke intelligent early warning device, early warning system and early warning method. The device is characterized in that an aerosol measuring light chamber, a particle high-efficiency filter, a trace gas monitoring chamber and a piezoelectric micropump are sequentially mounted in an inner cavity of an outer shell from front to back; a 650nm laser source, a first detection assembly, a 450nm laser source, a second detection assembly, a 850nm laser source, a third detection assembly and a temperature sensor are respectively arranged in the aerosol measurement light chamber; a piezoelectric quartz crystal detection matrix, a quartz wafer oscillation module and a high-precision frequency measurement module are arranged in the trace gas monitoring chamber; according to the system, a central server is respectively connected with a visual client and a plurality of mining smoke intelligent alarm devices; the method comprises the following steps: monitoring a plurality of monitoring areas by using a plurality of mining smoke intelligent alarm devices; and the central server processes the monitoring data and outputs a monitoring result. The device, the system and the method can accurately monitor smoke in a working place in real time.

The invention relates to the field of hybrid power transmission line fault maintenance, in particular to a sectional type multi-sensor detection hybrid power transmission line fault rapid positioning and diagnosing system, which comprises the following modules: a distributed fault positioning module, a fault diagnosis module, a fault diagnosis module and a fault diagnosis module, rapidly positioning a fault point according to the instantaneous traveling wave signal of the line by adopting a traveling wave measurement technology; and the data acquisition and transmission module is used for summarizing the line instantaneous traveling wave signal data acquired by each sensor and transmitting the data to the intelligent data analysis platform. Through the distributed fault positioning module, a fault point can be quickly positioned in an extremely short time, the fault searching time is greatly shortened, the emergency response capability of a power grid is improved, the overall coverage of the whole hybrid power transmission line is ensured through the installation mode of the distributed sensor, and no matter which position the fault occurs, the fault point can be quickly positioned. And fault signals can be captured and processed in time.

The invention discloses a pipeline welding device based on a visual sensor and a welding method, and belongs to the technical field of pipeline welding. The pipeline welding device comprises a welding table, a welding robot is arranged on the welding table, a driving mechanism for driving the welding robot to translate in the length direction of the welding table is arranged on the welding table, and a welding gun assembly is installed on the welding robot; the welding gun assembly comprises a fixed mounting frame, a molten pool monitoring sensor, a welding gun, a welding seam contour sensor and a welding seam positioning sensor. The invention further discloses a pipeline welding method based on the visual sensor. A to-be-welded pipeline can be photographed through a welding seam positioning sensor, then the position of a welding seam is recognized through an AI algorithm model, and a 2D camera is adopted for workpiece recognition and welding seam coarse positioning before welding; the welding seam position is recognized through the AI algorithm model and converted into the position coordinates of the welding robot, the welding robot is guided to conduct welding seam scanning, the positioning precision can be better than 5 mm, and the welding precision is improved.

The embodiment of the invention provides a fatigue detection method and device based on time sequence self-attention and auxiliary supervision. Multi-dimensional features such as the eyelid aspect ratio, the mouth opening degree and the head turning angle of the face of a driver are extracted through face detection and key point positioning. A multi-layer self-attention coding network is innovatively constructed, two branches of trunk feature extraction and auxiliary supervision are included, and a dynamic change rule of a face state is captured by using a time sequence self-attention mechanism. An auxiliary supervision branch is introduced to predict each face parameter state, and a feature learning process is optimized through a multi-task loss function. According to the method, the limitation of traditional single feature analysis is broken through, accurate recognition of the fatigue state of the driver is achieved, and reliable technical support is provided for improving road traffic safety.

The invention relates to the field of intelligent security and protection monitoring, and particularly discloses an intelligent security and protection monitoring platform and system based on a computer, and the method comprises the steps that an information collection unit collects a security event type, a dangerous event type and real-time monitoring data obtained by a plurality of cameras; the information processing unit screens and acquires abnormal behavior information according to the preprocessed security event type and dangerous event type and marks the abnormal behavior information; performing abnormal behavior information identification on the plurality of cameras; the network construction unit determines a target camera and constructs a target camera network; the security and protection monitoring unit obtains a real-time monitoring data set of all target cameras in the target camera network within the time period; the information analysis unit performs target detection analysis on the real-time monitoring data set acquired by the plurality of target cameras, and judges whether an abnormal target individual exists in a target frame: if yes, acquiring a feature coding sequence of the abnormal target individual under the plurality of different cameras; and if not, continuing security and protection monitoring.

The invention discloses a time-space multi-granularity fusion remote sensing image change detection method and device, and the method comprises the steps: inputting an obtained remote sensing image data set into a constructed time-space multi-granularity fusion network (STMINet) for training, obtaining a trained STMINet, and storing the trained STMINet; and inputting a remote sensing image to be processed, and performing prediction through the trained STMINet to obtain a dual-time-phase change prediction map.

The invention relates to a precision detection device for gear processing, which is suitable for detecting the dynamic performance and single tooth precision of a gear, and comprises a base, a rotating bracket, a fixed block, a motor, a reduction gearbox, a test gear, a to-be-tested gear, a main measuring rod, a plurality of measuring rods and the like. High-precision detection of gear dynamic balance, vibration parameters and the tooth outer end and the contour line of a single gear is achieved, the reduction gearbox reduces the rotating speed of the gear and strengthens torque control, and the gear thickness difference design is matched, so that the multiple measuring rods can slide along the tooth outer end of the gear, tooth surface deformation and the contact state are accurately detected, and the device is made of high-carbon low-alloy tool steel. The gear detection device has high strength and wear resistance, adapts to various gear specifications, can output detection data and analysis results in real time, is simple to operate, high in detection precision and high in applicability, and can be widely applied to the fields of gear manufacturing, quality detection and maintenance.

The invention provides a reference marking system, a three-dimensional reconstruction method and electronic equipment, and relates to the technical field of three-dimensional reconstruction. The marking assembly comprises at least four non-coplanar markers with different target features, and the target features of the markers have projection stability; the marking assembly has full-view projection specificity, and two-dimensional projection images of the marking assembly are in one-to-one correspondence with three-dimensional space postures of the marking assembly. The reference marking system is suitable for X-ray imaging and natural light imaging, and can realize accurate identification of projection positions of all markers, so that accurate spatial position and attitude information can be provided for three-dimensional reconstruction, high-precision three-dimensional reconstruction can be performed from a single or sparse angle, and the spatial positioning precision and the reconstruction efficiency are improved.

The invention discloses a rock mineral content detection system and a rock mineral content detection method for geological survey, which are characterized in that after image features and spectral features of rocks and ores are extracted, internal generality of the image features and the spectral features of the rocks and ores is mined by using a data processing technology based on deep learning, and the internal generality of the image features and the spectral features of the rocks and ores is used as a mask; according to the method, unique information in rock and ore image features and spectral features is emphasized, and the general information and the unique information of the rock and ore image features and the spectral features are comprehensively utilized through compensation type interactive fusion, so that accurate identification of rock and ore categories is realized. In this way, the problem of information loss in the multi-modal feature fusion process can be effectively solved, and therefore the accuracy of rock and ore classification and the calculation precision of the mineral content are improved.

The invention discloses a railway construction protection method and system, and the method comprises the steps: firstly, setting hyper-parameters based on a construction operation position; secondly, acquiring video stream data of a construction scene in real time based on an unmanned aerial vehicle; secondly, segmenting a railway in the obtained video stream data by adopting an instance segmentation algorithm, and obtaining pixel point coordinates of an uplink track and pixel point coordinates of a downlink track; then, based on the pixel point coordinates of the uplink track and the pixel point coordinates of the downlink track, a fitting straight line of the uplink track and the downlink track is calculated; then, based on the fitting straight line of the uplink track and the downlink track, an electronic fence is automatically generated; and finally, based on the coordinate position of the constructor and the electronic fence, whether the constructor breaks into the dangerous area is judged. According to the method, the instance segmentation algorithm is adopted to segment the railway, the segmentation effect is good, manual drawing of an electronic fence is not needed, and only the unmanned aerial vehicle needs to be carried to a designated construction site without external cooperation.

The invention discloses an end-to-end cervical cell classification integrated framework. The system mainly comprises four modules: an enhancement module for expanding and preprocessing an original data set; the fusion module is used for training an improved primary learner on the data set and fusing, outputting and generating a new data set; the purification module is used for purifying the new data set to obtain an integration result of the primary learner and retaining misclassified samples; and the relearning module is used for carrying out relearning by using a secondary learner and carrying out final classification. In addition, a self-adaptive data enhancement method is provided in the training stage of the primary learner and the secondary learner. According to the method, through stage-by-stage fine-grained optimization, the problems that existing cervical cell data sets are unbalanced in category distribution and rare in number are solved, and meanwhile, the classification precision is remarkably improved by emphatically learning easily-confused image features through an integrated framework. Experiments show that the method can effectively assist in cervical cancer screening, the workload of pathologists is greatly reduced, and the method has good application value and huge social benefits.

The invention discloses a multi-parameter fusion gas building gas leakage early warning method and equipment, and the method comprises the steps: continuously collecting the real-time flow and real-time pressure information of a gas meter for measuring the gas consumption of gas equipment in a set time period through a computer, and recording the collection time, the real-time flow and the real-time pressure information of the gas meter according to a time sequence; forming an initial data set; setting a pressure threshold, determining a pressure interval division method, classifying the real-time pressure of the initial data set, forming an initial data subset, continuing to collect the real-time pressure data and supplementing the real-time pressure data to the initial data subset, forming a real-time data subset, and constructing a Poisson random variable model; analyzing the distribution condition of the flow characteristic estimation values in the real-time data subset; and determining the distribution mean values of the flow characteristic estimation values in the real-time data subset, and determining whether the gas supply system leaks or not according to the deviation between the mean values. According to the invention, the accuracy of the early warning system is improved, so that the gas is safer to use.

The invention discloses a CNN-based intelligent water pollution identification system in the field of environmental protection and intelligent monitoring, and the system comprises a data collection module, a data processing module, an interference elimination module, and a pollution identification module which is used for carrying out the accurate identification of pollutants through a convolutional neural network. The judgment logic of the interference elimination module is as follows: if the comparison data of the real-time data and the standard data are consistent, the surface of the water body is pollution-free; when the motion trail presents a feature from bottom to top, animal activity or bubble generation is indicated, and the motion trail is identified as an interference item; and when the motion trail is synchronous with the water surface fluctuation and changes along with the change of the sun position, the motion trail caused by the light and shadow change is identified as the interference item. According to the invention, through a plurality of modules, accurate identification of water pollutants is realized, interference of non-pollution factors is effectively eliminated, and monitoring accuracy and real-time performance are improved.

The invention discloses a gyrotron abnormal test data identification method and system, and the method comprises the steps: 1, constructing a gyrotron test data set, and initializing the parameters of a feature extractor and a Gaussian mixture model; 2, preheating training is conducted, characterization learning is conducted on gyrotron test data through a feature extractor, potential features of the data are obtained, whether preheating training is completed or not is continuously judged, if not, a learning target is continuously optimized, and if yes, the step 3 is executed, and the feature extractor is a deep neural network; 3, after preheating is finished, a Gaussian mixture model is used for conducting distribution modeling on potential characteristics of gyrotron test data; 4, calculating the distribution confidence coefficient of the gyrotron test data by using a Gaussian mixture model, and identifying the gyrotron abnormal test data; and 5, repeating the steps 2-4 until the target training function is converged. According to the method, the accuracy of a gyrotron fault identification task is remarkably improved, abnormal data points in gyrotron test data can be identified, and a powerful guarantee is provided for stable operation of an ECRH system.

The invention provides a large yellow croaker body surface white spot disease image acquisition device based on a YOLOv5 algorithm. The large yellow croaker body surface white spot disease image acquisition device comprises a flow velocity simulation water tank (1); the flow velocity simulation water tank comprises a large yellow croaker image acquisition area (5), a flow velocity control area (25) and a water environment control area (13); an image data acquisition system is arranged at the large yellow croaker image acquisition area and comprises a plurality of cameras connected with a computer; the flow velocity simulation water tank firstly limits the swimming action of the detected pseudosciaena crocea to enable the action of the pseudosciaena crocea to be matched with the working parameters of the image data acquisition system, and then the image data acquisition system acquires the body surface image of the pseudosciaena crocea, analyzes the image based on a YOLOv5 algorithm, or trains the YOLOv5 algorithm by the acquired image; according to the invention, a large amount of independent, multi-angle and clear image data of the sick pseudosciaena crocea can be acquired, and the image data of the sick pseudosciaena crocea under the conditions of different turbidity, different illumination intensities and group movement in a culture environment can be simulated, so that the training effect of the YOLOv5 algorithm is more remarkable.

The invention relates to the technical field of abnormal value detection methods of big data, in particular to an abnormal value detection method and system based on agricultural big data, and the method comprises the following steps: 1, obtaining agricultural big data from a spatial data service; 2, inputting the training data into a preset abnormal value detection model for training; 3, inputting verification data into the abnormal value detection model for verification; step 4, comparing the training result of the abnormal value detection with a preset detection threshold value; 5, generating an optimized abnormal value detection model based on a training result of abnormal value detection; step 6, performing abnormal value detection on the detection data acquired in real time by using the optimized abnormal value detection model, generating a detection result containing a time position, a space position and an abnormal type of an abnormal value, and improving the detection accuracy to 95% or above through accurate extraction of spatial and temporal features; and the data integrity reaches 99.5% by depending on a multi-layer preprocessing strategy.

The invention discloses a wafer graph defect pattern recognition method based on improved ResNet18 and a related device. The method comprises the following steps: acquiring a wafer graph to be recognized; inputting the to-be-identified wafer graph into a pre-trained wafer graph defect mode identification model, and outputting an identification result; wherein the wafer graph defect mode recognition model is obtained by training improved ResNet18 by using training data, and the training data comprises wafer graphs of different defect modes and corresponding defect mode labels; the improved ResNet18 comprises a selective convolution kernel module, the selective convolution kernel module is used for replacing a first convolution layer of a ResNet18 backbone network, and the selective convolution kernel module is used for adaptively extracting defect features of different sizes of a wafer graph. The invention aims to enhance the capability of extracting defect features of different sizes and types in a wafer graph, so as to improve the accuracy of wafer defect mode recognition.

The invention provides a salt rock expansion point identification method and electronic equipment, and relates to the technical field of geological engineering monitoring. The method comprises the following steps: carrying out sliding sampling on acoustic emission data in an initial search interval according to a first preset window length to obtain a plurality of initial window data segments; determining an initial expansion point position according to the plurality of initial window data segments; determining a target search interval according to the initial capacity expansion point position; wherein the target search interval is smaller than the initial search interval; performing sliding sampling on the acoustic emission data in the target search interval with a second preset window length to obtain a plurality of target window data segments; wherein the second preset window length is smaller than the first preset window length; and determining a target expansion point position according to the plurality of target window data segments. Acoustic emission data is adopted as a data source and can be used for real-time monitoring; and meanwhile, a two-section identification method of rough search and fine search is adopted, so that accurate identification of the expansion point can be realized.

The invention discloses a facial expression recognition method and device based on multimode attitude measurement learning, and a storage medium, relates to the technical field of facial expression recognition, and solves the technical problems of weak generalization ability, insufficient cross-modal feature fusion and easy forgetting of pre-training knowledge due to data scarcity in the existing facial expression recognition technology. The method comprises the following steps: acquiring image data containing facial expressions, preprocessing the image data, constructing a text prompt word set, and constructing triple training samples of anchor points, positive samples and negative samples; a visual cue word vector is designed to be injected into a Transform layer of a pre-trained CLIP-ViT-B / 32 model, and model trunk parameters are frozen to only train visual prompt and LayerNorm layer parameters; triple loss and classification loss combined optimization is adopted; in the reasoning process, the expression category is determined by calculating the similarity between the to-be-recognized image features and various expression text prompt features, pre-training semantic knowledge and cross-modal adaptation ability of CLIP are fully utilized, and the expression recognition precision, robustness and zero sample generalization ability are remarkably improved.

This invention provides a method and system for constructing a 3D model of a construction site by combining multiple data sources. It relates to the field of data processing technology. The method includes: retrieving satellite imagery at a preset resolution to obtain a full-area stereo image pair dataset; performing radiometric correction to generate a regional surface model and a regional elevation model; performing raster-level modeling defect detection to locate distributed blind zone blocks; fitting obstacle avoidance paths to generate optimized obstacle avoidance flight trajectories; driving a UAV to perform multi-angle surround scanning to directionally collect dense point clouds in the distributed blind zones; constructing a distributed blind zone model; and performing microscopic overlay to output a 3D model of the construction site. This invention solves the technical problems of existing construction site modeling methods, which have limitations in coverage, modeling efficiency, and data real-time performance, and rely on a single data source, failing to provide a comprehensive and accurate 3D model of the construction site, thus affecting construction progress and decision-making.

The invention relates to an electric power inspection image recognition method and device, and a storage medium, and the method comprises the following steps: training a basic model through employing a large-scale image data set; performing fine adjustment on a specific layer of the trained basic model by using the electric power inspection image data set subjected to data enhancement processing; obtaining a to-be-identified electric power inspection image, and extracting multi-scale low-level features and high-level features in a residual connection mode by using the fine-tuned basic model; for the multi-scale low-level features and the multi-scale high-level features, multi-scale feature fusion is realized based on a self-attention mechanism and a channel attention mechanism, and fusion features are obtained; and based on the fusion feature, obtaining a predicted label through a classifier, and realizing electric power inspection image recognition. Compared with the prior art, the method has the advantages of being suitable for a scene with a small power inspection image data set, good in feature fusion and multi-scale processing effect and the like.

The invention provides an adhesive defect identification method based on an MCED-YOLOv7 network, and the method comprises the steps: obtaining a to-be-identified image, and carrying out the data image processing through the linear interpolation; carrying out image feature processing by adopting an exponential amplitude limiting gain algorithm; based on a YOLOv7 architecture, an MLCA attention mechanism is introduced behind an ELAN module, and a CBAM attention mechanism is added to the convolution of an SPPCSPC module; an original CIOU is improved into an EIOU, a DYHEAD target detection head is added, and an MCED-YOLOv7 detection network is constructed and used for carrying out defect identification on an image to be identified. According to the invention, accurate detection of a normal adhesive, top void and base void in the insulation board can be realized, the accuracy and robustness of the detection network are improved, and the phenomena of false detection and excessive detection are significantly improved.

The invention proposes a seismic signal P-wave detection method based on a PPNet network, belonging to the technical field of seismic signal processing; the network of the invention can realize the seismic P-wave picking with higher precision and lower error; combined with UNet++ encoding and decoding, a lightweight P-wave arrival time picking network PPNet (P‑wave Pyramid Network) with seismic phase feature analysis and fusion capability is designed by integrating feature filters; the encoder module adopts a convolution layer with a large convolution kernel and a low number of channels to perform deep feature extraction on the input seismic signal, enhance the phase arrival time weight in the signal sequence, and suppress and filter other interference information; a feature fusion mechanism is added in the feature restoration process of the decoder module to complete the feature information and realize the accurate identification of each time step in the seismic signal sequence; feature filters are added only to the three downsampling modules after the encoder to process the output feature sequence, refine the P-wave arrival time feature, and improve the arrival time picking accuracy; the invention is applied to seismic P-wave picking.

The invention relates to the field of remote sensing image processing, and discloses an urban functional area identification method and system based on a remote sensing image spatial adjacency relation, and the method comprises the steps: S1, collecting remote sensing image data of a research area; constructing a ground feature element extraction model to perform ground feature element feature extraction and classification identification on the remote sensing image data, and obtaining a feature map; s2, constructing a graph neural network model, and constructing graph structure data of a network topology structure by taking the ground feature element objects as graph nodes and taking the spatial adjacency of the adjacent ground feature element objects as edges; the graph neural network model extracts feature vectors of graph nodes by using a graph neural network graph attention network; and S3, outputting a functional area identification result according to the urban functional area cells. According to the method, based on remote sensing image data classification, the ground feature elements are identified and the urban functional area cells are divided, the ground feature elements in the urban functional area cells and the spatial adjacency relation are deeply excavated, and high-precision identification and division of the urban functional areas are achieved.

This invention relates to the field of image data processing technology, specifically to a deep learning-based intelligent identification system and method for blasted rock mass size. The method includes: acquiring images of rock accumulation at a blasting site using an image acquisition device; importing these images into a deep learning-based intelligent identification system for blasted rock mass size; analyzing the blasted rock mass size data; generating blasted rock mass size curves; and performing a reasonable analysis of the blasting effect. The intelligent identification system for blasted rock mass size includes an image preprocessing module, a rock mass size analysis module, and a result visualization module. The image preprocessing module performs image enhancement, noise reduction, and segmentation; after feature extraction via a convolutional neural network, the rock mass size identification module performs rock mass segmentation, classification, and size measurement; the rock mass size analysis module statistically generates distribution curves; and the visualization module presents the results. This identification system achieves automatic, rapid, and accurate identification of rock mass size in complex scenes, effectively improving the efficiency and accuracy of blasting effect evaluation.