6735results about How to "Fully automated" patented technology

The invention provides an on-line quality monitoring method and system based on optical multispectral fusion, and relates to the technical field of multispectral fusion, and the method comprises the steps: collecting visible light and infrared multispectral images of an object to be measured, carrying out the preprocessing and registration, inputting the images into a variational auto-encoder, and carrying out the probability distribution modeling, and obtaining a spectrum basis vector and an initial abundance graph; and constructing a multi-scale morphological feature pyramid, performing graph segmentation in combination with spatial information to obtain a local consistency region, and fusing the local consistency region with the initial abundance graph to obtain an enhanced abundance feature. And fusing the spectrum basis vector and the enhanced abundance feature by adopting a multi-head cross attention mechanism to obtain a multi-spectrum fusion feature. And inputting the fusion features into a dynamic neural network, analyzing a topological connection relationship between nodes, obtaining a structured representation, performing discriminative enhancement, calculating node significance distribution, determining a quality defect area, analyzing a formation mechanism of the quality defect area, and finally generating a quality monitoring report.

The invention discloses a concrete bridge crack detection method based on computer vision, and relates to bridge detection, and the method comprises the steps: collecting a crack image, GPS data and a depth image of a concrete bridge; according to the GPS data, establishing a mapping relation between the pixel coordinates and the fracture physical space coordinates; based on feature matching of ORB features and a registration optimization algorithm based on mutual information, obtaining alignment of the crack image and the depth image at a pixel level, and obtaining a color crack image and a depth crack image; a MaskR-CNN model is adopted to extract a pixel-level crack segmentation mask of the crack region; according to the crack segmentation mask and the registered depth crack image, depth information of a crack region is estimated through a depth completion algorithm based on a conditional generative adversarial network (CGAN), and a crack depth distribution map is obtained; according to the crack depth distribution map, geometric parameters of the crack are extracted through a morphological algorithm and a central axis transformation algorithm. Aiming at the low concrete bridge crack detection precision in the prior art, the detection precision is improved.

The invention provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention provides an unmanned aerial vehicle accessory intelligent identification and access method and system based on a sensor, and relates to the technical field of unmanned aerial vehicle accessory management, and the method comprises the steps: collecting the three-dimensional contour information of unmanned aerial vehicle accessories, generating standard point cloud data, carrying out the feature extraction, and carrying out the feature alignment and interaction, generating a high-dimensional fusion feature matrix of the target unmanned aerial vehicle accessories; based on the feature pyramid network, constructing multi-scale feature representation, generating hierarchical feature descriptors, performing similarity calculation, generating a similarity score matrix, performing optimal matching, establishing an adaptive verification rule base and performing accessory verification; calculating an optimal action sequence of an access path based on model information, mapping the optimal action sequence into a motion track parameter of a mechanical arm, collecting multi-point stress data, predicting the optimal clamping force, establishing an accessory service life prediction model, performing prediction in combination with historical data, and generating an accessory state evaluation report and a maintenance suggestion.

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention relates to the field of data processing, in particular to a security evaluation method based on a large language model and a related device. The method comprises the following steps: identifying a security evaluation dimension matched with a large language model; generating an autonomous test case of the large language model based on the security evaluation dimension; the autonomous test case comprises a normal user and an abnormal case; the information content in the abnormal use case corresponds to the type of the large language model; the autonomous test case is input into the large language model to execute a model function, and a response result of the large language model to the autonomous test case is obtained; and generating a security evaluation report for the large language model based on the response result. According to the method, the autonomous test case is adopted to actively evaluate the safety of the model, the automatic comprehensive evaluation of the model is realized, the model safety evaluation efficiency and accuracy are improved, the adaptability to models in different fields and scales is enhanced, the automation and continuous optimization of safety evaluation are realized, and the operation safety of the model is ensured.

The invention discloses an intelligent textile production line control method and system, and relates to the technical field of industrial automation, and the method comprises the steps: collecting operation data in a textile production process in real time, and carrying out the data preprocessing; dynamically adjusting process parameters of the textile production line based on the preprocessed data; detecting the quality state in the textile production process in real time, and adjusting the operation of the production line based on the detection result; constructing a multi-feature fusion fault prediction model, and predicting the operation state of the production line equipment; potential faults are recognized in advance according to prediction results, and the operation mode of the production line is adjusted. The method combines multi-feature fusion analysis, dynamically optimizes the equipment operation state, identifies and adjusts potential faults in advance, improves the production quality and efficiency, reduces the maintenance cost and the shutdown risk, is significantly superior to a traditional method, and provides powerful technical support for intelligent upgrading of the textile industry.

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention relates to the technical field of water quality and water level monitoring, and discloses a water quality and water level monitoring method for a water conservancy project, which comprises the following steps: S1, arranging a multi-parameter water quality and water level monitoring sensor at a key position of the water conservancy project; s2, performing real-time processing on the data acquired by the multiple sensors based on a data fusion algorithm, wherein the data fusion algorithm is based on a Kalman filtering model; s3, acquiring external data through an environment sensor, and dynamically adjusting a water level and a water quality monitoring threshold value based on environment change; s4, predicting the water level and the water quality by using a deep learning algorithm based on LSTM (long short-term memory network); and S5, if the monitoring data exceed the dynamic threshold value, automatically triggering an early warning mechanism. By arranging the multi-parameter water quality sensor and the water level sensor at the key positions of the water conservancy project, the water quality and the water level can be comprehensively monitored, the coverage range and the precision of the monitoring system are greatly improved, and the complex monitoring requirements of multi-source pollution can be effectively met.

The invention discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention relates to the technical field of brushless motor testing, and discloses a test adjusting method and system for an intelligent brushless motor, and the method comprises the steps: carrying out the real-time monitoring of the current, rotating speed and load characteristics of the brushless motor, obtaining the operation data of the motor, and obtaining a motor working mode recognition result; constructing a motor parameter hierarchical optimization scheme; performing dynamic optimization to obtain a motor adaptive control parameter set; performing rolling optimization solution to obtain a motor prediction control instruction sequence; performing multi-objective optimization calculation to obtain a motor multi-performance balance adjustment scheme; a motor multi-performance balance adjustment scheme is integrated into a digital twin test platform, simulation verification is carried out on the performance of the brushless motor under different working conditions, a motor test adjustment strategy is obtained, the working mode of the motor can be recognized in real time, parameter optimization and performance prediction can be carried out based on historical data and the current state, and the stability of the motor test adjustment strategy is improved. Therefore, the optimal operation state of the motor under different working conditions is realized.

The invention relates to a metropolitan area network traffic prediction application method based on a neural network, and belongs to the technical field of network traffic. Dividing a training set, a verification set and a test set; 70%, 20% and 10% of data are used for model training, optimization and evaluation; inputting the traffic data into the double-layer stacked LSTM network, obtaining hidden state information and extracting time sequence characteristics and dependency of network traffic; capturing a long-term and short-term dependency relationship in the flow by using an encoder; global dependency features are extracted through a self-attention mechanism; gradually predicting flow data of a plurality of time points in the future by adopting a decoder; an early stop strategy is adopted in model training to prevent overfitting; carrying out anti-standardization processing on a model prediction result, and recovering an original scale; and evaluating a model prediction result. According to the invention, training and optimization of the deep learning model are carried out on the historical data and the feature data of the metropolitan area network, so that the accuracy of IP network traffic prediction is remarkably improved.

The invention relates to a pipeline detection robot defect identification method based on image processing, which comprises the following steps: carrying out continuous image acquisition on the inner wall of a pipeline through image acquisition equipment on a pipeline robot to obtain image data of the inner wall of the pipeline; performing image preprocessing on the pipeline inner wall image data; pipeline defects in the pipeline inner wall image data after image preprocessing are detected, and image segmentation is carried out on a defect area; and carrying out feature extraction on the defect area, carrying out defect classification, and outputting an identification classification result. According to the invention, based on algorithms of image processing, artificial intelligence and machine learning, defects of cracks, corrosion, leakage and the like in the pipeline can be automatically identified, the limitation of manual detection is solved, automatic and intelligent detection is realized, the technical effects are mainly embodied in the aspects of improving the detection precision and efficiency and the ability to adapt to different pipeline environments, and the method is suitable for popularization and application. High application and popularization values are realized.

The invention relates to a digital acupuncture dynamic control optimization method. According to the technical scheme, the method comprises the steps that real-time physiological data, including the heart rate, the oxyhemoglobin saturation, the skin temperature and the muscle tension, of a patient are collected through a wearable device and a sensor; performing cleaning, denoising and feature extraction on the multi-modal physiological data by using a data preprocessing algorithm; a personalized digital acupuncture model is established, and the physical condition and acupoint sensitivity of a patient are dynamically evaluated; adjusting acupuncture output parameters, control frequency, pulse width and current intensity according to real-time feedback of a patient by adopting a self-adaptive fuzzy control algorithm through the obtained personalized digital acupuncture model; multi-channel biofeedback equipment including EEG, EMG and ECG is integrated, and instant response of a patient is monitored; through a closed-loop control algorithm, biological feedback data are input into the control model in real time, and dynamic adjustment of acupuncture and moxibustion parameters is controlled; a deep reinforcement learning algorithm is introduced into acupuncture therapy.

The invention provides an automatic assembling method and system for an automobile safety belt pretensioner based on machine vision, and relates to the technical field of automatic assembly.The method comprises the steps that firstly, images of standard parts of the pretensioner are collected, geometrical characteristic parameters are extracted, and the mapping relation from two-dimensional image coordinates to three-dimensional space coordinates is established; calculating coordinates of an assembly reference point and an assembly path point; acquiring a real-time image of a to-be-assembled part, extracting feature data, matching the feature data with a standard part, calculating a position deviation value and an angle deviation value, and updating an assembly path point coordinate; and controlling the robot to execute assembly, and dynamically adjusting the assembly speed and the assembly strength of the robot based on the assembly state evaluation index after data fusion of the force sensor and the displacement sensor until automatic assembly is completed.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention provides a distributed storage and data warehouse permission synchronization method and system, and relates to the technical field of permission management, and the method comprises the steps: analyzing user operation data of distributed storage, training a machine learning model, and generating a directory permission intelligent mapping table; analyzing the structure information of the data table in the data warehouse, and generating a multi-dimensional table authority evaluation model; generating a self-adaptive permission rule base by combining the two, and deploying the self-adaptive permission rule base to each node of distributed storage; when the permission is changed, the system calculates an influence range according to the self-adaptive permission rule base and generates a permission updating instruction sequence; and the system monitors the instruction execution state, identifies abnormal nodes, and performs repair based on the permission propagation tree to ensure the permission consistency. According to the method, automatic synchronization of distributed storage and data warehouse authority can be realized, the authority management efficiency is improved, the manual intervention cost is reduced, and the data security is enhanced.

The invention provides a building measurement method and system based on an intelligent robot, and relates to the technical field of intelligent robots, and the method comprises the steps: deploying the intelligent robot to a to-be-measured building area; determining a final driving path of the intelligent robot according to the layout of the to-be-detected building area; the intelligent robot automatically runs along the final running path, and acquires three-dimensional point cloud data, image information and geographic position information of the building environment by using an internal sensor assembly; and processing the three-dimensional point cloud data, the image information and the geographic position information of the building environment, including point cloud splicing, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building. By automatically collecting and processing data, the building three-dimensional model and the detailed measurement report are efficiently generated, the measurement precision and efficiency are improved, and the labor cost is reduced.

The present disclosure provides a control system, method, apparatus, medium and program product for a welding process. The control system comprises a sensor module configured to collect data in the welding process; the data analysis module is configured to pre-process data in the welding process, extract key features from the pre-processed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification through a machine learning model based on the comprehensive features to identify defect features; the knowledge graph module is configured to obtain a repair strategy corresponding to the defect characteristics according to the defect characteristics and by utilizing a knowledge graph; the control module is configured to transmit the data received from the sensor module in the welding process to the data analysis module, receive the repair strategy and generate a repair instruction based on the repair strategy; and the repairing module is configured to automatically adjust welding parameters or control an executing mechanism to conduct repairing operation based on the repairing instruction.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

The invention discloses an improved YOLOv11 crack identification and quantification method for a strip mine area and a slope thereof. The method comprises the steps of data preprocessing, model improvement and adaptability adjustment, initial identification model training, crack detection by applying a target identification model, local area identification and post-processing. According to the method, technical means such as a feature extraction network improvement method based on an AReLU function and an ACmix model and a self-adaptive local threshold segmentation algorithm based on a gray scale standard deviation are introduced, and efficient and accurate recognition of cracks is achieved. According to the method, the automation degree of crack detection is improved, the labor cost and the time cost are reduced, and the method is of great significance to safety monitoring of the strip mine area.