Mine unsafe behavior AI intelligent identification system

Abstract

The invention discloses a mine unsafe behavior AI intelligent identification system, and aims to solve the problem of mine safety supervision. The system comprises a headquarters training center and a mine end system. The headquarters training center carries out model training and data processing, including construction of hardware expansion, a cloud platform, an AI enabling platform, a mine vision large model and the like; the mine end is responsible for field data acquisition and primary processing. A camera is installed in a key area of a mine to collect video data, and model training is carried out by combining sensor data and utilizing multiple frames and tools of an AI enabling platform. The system has a real-time monitoring function, unsafe behaviors and hidden dangers such as personnel invasion and belt deviation can be accurately recognized, an alarm is given in time, and equipment is controlled in a linkage mode. And meanwhile, multi-system linkage is realized. New scene enabling is provided, a perfect safety guarantee system is provided, data safety and stable operation of the system are guaranteed, and the intelligent safety management level of the mine is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and coal mine safety management technology, and in particular to an AI-powered intelligent identification system for unsafe behaviors in mines. Background Technology

[0002] As a traditional energy industry, coal mines face a complex situation and challenges, including intertwined safety risks, diverse production processes, complex working environments, and lagging technological development. In terms of safety management, most coal mine accidents are minor incidents, the root cause of which is unsafe behavior by workers driven by a mentality of taking chances, seeking shortcuts, carelessness, and recklessness. According to relevant statistics, in recent years, a high percentage of coal mine accidents in my country have been caused by unsafe acts such as violations of operating procedures and failure to comply with safety regulations. Traditionally, the supervision of unsafe acts relies mainly on safety management personnel. However, this approach is limited by human energy and visibility, resulting in high manpower requirements, limited supervisory personnel capacity, low efficiency, and a lack of transparency and fairness in the supervision process. Consequently, unsafe acts are often "unseen, undetected, and unmanageable," posing a significant threat to safe production.

[0003] Rule-based and threshold-based monitoring systems: Early coal mine safety monitoring relied heavily on rule-based and threshold-based systems, which used fixed parameters to determine equipment operating status and personnel behavior. For example, monitoring belt conveyor speed would trigger an alarm if the speed exceeded a preset range. However, this approach is ill-suited to the complex and ever-changing mine environment and struggles to identify ambiguous or novel unsafe behaviors, such as personnel wandering abnormally in dangerous areas, making it difficult to accurately determine whether a safety risk exists.

[0004] Simple video surveillance systems: Although some coal mines have installed video surveillance systems, they only serve a retrospective purpose. Manually reviewing surveillance videos is inefficient and cannot detect and prevent unsafe behaviors in real time. In practical applications, with numerous monitoring screens, monitoring personnel are prone to fatigue, leading to many safety hazards being overlooked and preventing timely measures to avoid accidents.

[0005] With the rapid development of artificial intelligence (AI) technology, significant achievements have been made in fields such as deep learning and computer vision. AI technology has been widely applied and yielded positive results in industries such as security and transportation. For example, in urban security monitoring, AI-powered intelligent recognition systems can quickly and accurately identify abnormal behavior and criminal suspects. These successful applications provide new ideas for solving safety supervision challenges in the coal mining industry. Introducing AI technology into the field of coal mine safety, through learning from a large amount of mine operation videos and data, enables the system to intelligently identify unsafe behaviors, potentially breaking through the limitations of traditional safety supervision methods and achieving real-time, accurate monitoring and early warning of unsafe behaviors in mines.

[0006] The state attaches great importance to coal mine safety and has issued a series of strict policies and regulations, clearly stipulating that coal mining enterprises must strengthen the construction of safety monitoring systems and improve the level of intelligent safety management. Local coal authorities have also increased their supervision of coal mine safety, requiring coal mining enterprises to adopt advanced technologies to enhance their safety assurance capabilities. Against this backdrop, in order to meet policy and regulatory requirements, coal mining enterprises are actively seeking more efficient and intelligent safety supervision solutions, promoting the research and application of AI-based intelligent identification systems for unsafe behaviors in mines. Summary of the Invention

[0007] To achieve the above objectives, the present invention provides an AI-powered intelligent identification system for unsafe behaviors in mines.

[0008] An AI-powered intelligent identification system for unsafe behaviors in mines includes a headquarters training center system and a mine-side system that achieve data interaction and coordinated control through cloud-edge collaboration technology. The headquarters training center system includes a training center server cluster, a network equipment cluster, a cloud platform, an edge inference platform, an AI enabling platform (ModelArts), a large-scale vision model for mines, and an AI application platform (headquarters). The mining system includes edge inference devices for real-time data acquisition and preliminary data processing, as well as a mining AI application platform that synchronizes and communicates with the headquarters platform to enable local decision-making and control.

[0009] Furthermore, the training center server cluster includes training servers, gateway servers, compute node servers, management node servers, and object storage servers, providing computing, storage, and network resource support for the system.

[0010] Furthermore, the network equipment cluster includes AIROCE network switches, object storage switches, BMC switches, management service Tor switches, and core switches, constructing a high-speed and stable network communication environment.

[0011] Furthermore, the cloud platform consists of an infrastructure layer, a resource pool layer, a cloud service layer, a presentation layer, and a management domain, providing IaaS and PaaS services to achieve automated allocation and management of cloud resources.

[0012] Furthermore, the AI-enabled platform (ModelArts) provides end-to-end AI development and inference services, supports heterogeneous resource scheduling and management, and covers AI development, model training, inference services, multi-framework support, and unified model management functions.

[0013] Furthermore, the large-scale vision model for mines includes a development kit and a basic model. The basic model has AI atomic capabilities for object detection and semantic segmentation, and supports incremental training and self-iteration.

[0014] Furthermore, the AI ​​application platform (headquarters) has integrated dashboard, alarm management, and video management modules, and has corresponding requirements in terms of performance, security, reliability, compatibility, ease of use, and maintainability.

[0015] Furthermore, the system adopts a full-mesh fully connected networking approach, with servers supporting 100GE speeds. The training server is interconnected with the existing cloud platform via a 10GE network, and the AI ​​training platform supports resume training after interruption.

[0016] Furthermore, the system possesses new scenario-enabling capabilities and corresponding services, enabling it to identify and analyze new unsafe behaviors in the mining environment, thereby improving the system's adaptability and identification accuracy.

[0017] Furthermore, the system integrates with the emergency broadcasting system, with a response time of no more than 1 second, and uploads intelligent video monitoring data from the mine to the monitoring platform of the superior coal management authority.

[0018] Furthermore, the system adopts a distributed computing architecture, supports multi-task parallel processing, and can still maintain real-time responsiveness under high load.

[0019] Furthermore, the system has a self-learning function, which can continuously optimize and update the model based on new data, thereby improving recognition accuracy and reducing false alarm rate.

[0020] Furthermore, the system employs edge computing technology to maintain basic functionality even in the event of a network outage, ensuring continuous security monitoring.

[0021] Furthermore, the system possesses multimodal fusion capabilities, enabling it to simultaneously process and analyze multiple information sources such as video, audio, and sensor data, providing a more comprehensive security risk assessment.

[0022] Furthermore, the system develops AI algorithms and post-processing code logic that match the coal mine safety production process. It fully utilizes AI to monitor equipment operation, personnel posture, work procedures, and dangerous areas in video surveillance footage, and integrates them into scenario-based model code, making it more effective in identifying unsafe human behaviors.

[0023] The beneficial effects of this invention are: By deploying cameras in key areas of multiple systems, the system can monitor personnel behavior and the status of equipment and the environment in real time. In human-machine interaction scenarios such as coal mining machine operating areas, support installation and dismantling sites, tunneling support processes, main transport equipment operating parts, rail-guided and trackless auxiliary transport, and hoisting operations, the system accurately determines whether there are safety hazards on site. It then identifies unsafe behaviors and hazards such as personnel intrusion, non-standard operations, and non-standard on-site practices, promptly issuing alarms and triggering coordinated control, significantly reducing the risk of accidents. The AI ​​linkage function collaborates with multiple systems, accurately pushing information and controlling equipment based on alarms, improving emergency response speed and processing capabilities, and ensuring personnel safety. Real-time monitoring and fault warnings of equipment operating status can prevent production interruptions caused by equipment failures. For example, the equipment no-load operation identification function can adjust equipment operation in a timely manner to reduce energy consumption. Furthermore, through the analysis of production data, production processes can be optimized to improve overall production efficiency; the AI ​​application platform enables centralized management of all data, facilitating querying, statistics, and analysis. The dataset management module can uniformly process false alarm data, providing support for model optimization. In-depth data analysis uncovers potential security risks and production issues, providing a basis for decision-making. A comprehensive security protection system, from hardware to software, ensures system stability and data security. Features such as network security domain partitioning, VPC services, and security groups prevent external attacks and internal data leaks. Data backup, recovery, and encryption mechanisms ensure data integrity and availability, preventing production disruptions due to data loss or corruption. The flexible system architecture supports the development of new scenario algorithm models and functional expansion. Standardized hardware devices, comprehensive software maintenance tools, and clearly defined third-party software licenses reduce maintenance difficulty and costs, ensuring continuous and stable system operation. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the software system architecture of the present invention; Figure 3 This is a schematic diagram of the data flow in this invention; Figure 4 This is a schematic diagram illustrating the implementation path of personnel intrusion detection in a coal mining machine according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the implementation path for monitoring unauthorized operations of equipment components according to an embodiment of the present invention. Figure 6This is a schematic diagram illustrating the implementation path of the auxiliary transportation vehicle and pedestrian monitoring algorithm according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the path for identifying personnel entering hazardous areas while tensioning anchor cables, according to an embodiment of the present invention. Figure 8 This is a schematic diagram illustrating the monitoring implementation path for the maintenance specifications of a monorail crane according to an embodiment of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0027] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0028] To provide the public with a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments. However, those skilled in the art will fully understand the invention even without these detailed descriptions. Furthermore, to avoid unnecessary misunderstanding of the essence of the invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0029] See Figures 1 to 3 As shown I. System Deployment and Setup (I) Deployment of the Headquarters Training Center System Hardware deployment In the headquarters server room, various servers and network equipment were deployed according to the plan. PowerEdge PR420KIG2 training servers, Huawei customized gateway servers, compute node servers, management node servers, object storage servers, etc., were installed in the racks according to the rack plan, ensuring that the physical connections between the servers were correct, such as power connections and network connections.

[0030] Install network equipment, including AIROCE network switches, object storage switches, BMC switches, management service Tor switches, and core switches, configure ports and connect links, and adopt a full-mesh networking approach to ensure high-speed and stable communication between servers and switches.

[0031] Software installation and configuration Install the cloud platform and deploy it according to an architecture consisting of an infrastructure layer, a resource pool layer, a cloud service layer, a presentation layer, and a management domain. At the infrastructure layer, complete the initial configuration of physical facilities and network devices; at the resource pool layer, divide hardware devices into resource pools such as computing, storage, and network, and configure the corresponding parameters; at the cloud service layer, install IaaS and PaaS service components and configure automated allocation and management policies for cloud resources; at the presentation layer, deploy self-service and management interfaces and open API interfaces; at the management domain, configure operation and maintenance management functions, including tenant management, workflow approval, monitoring, and alerts.

[0032] Install the edge inference platform, perform system initialization configuration, and ensure that it can support the deployment of AI applications to edge nodes with user-specified resource specifications and number of instances, and realize functions such as one-click deployment, unified management, status query, online monitoring and application updates.

[0033] Install the AI ​​enabling platform (ModelArts) and configure its core functions, including AI development, model training, inference services, multi-framework support, and unified model management. Configure its key capabilities, such as support for multiple development frameworks and tools, and configure high-performance distributed training, hyperparameter search, and model architecture search.

[0034] Deploy a large-scale vision model for the mine, install the development kit, and configure functions such as dataset management, application management, application optimization, service management, cloud-edge collaboration, asset management, and system management. Load the basic model, including AI atomic capabilities such as object detection, semantic segmentation, target tracking, pose estimation, image classification, and video classification, and perform incremental training and self-iterative configuration.

[0035] Install the AI ​​application platform (headquarters), configuring functional modules such as integrated dashboard, alarm management, video management, statistical analysis, device management, model management, configuration management, and access control. Set non-functional requirements, such as performance optimization, security configuration, reliability assurance, compatibility settings, usability design, and maintainability management.

[0036] (II) Deployment of Mine-End Systems Edge inference device installation Edge inference devices were installed at suitable locations in each mine, ensuring stable operation and effective connection with on-site cameras, sensors, and other equipment. The devices were then debugged and configured to guarantee real-time data acquisition.

[0037] Deployment of AI application platform in mining Install the mining-side AI application platform on the mining server and configure data synchronization and communication with the headquarters' AI application platform. Configure the mining-side platform's local decision-making function to enable real-time processing and decision-making of on-site data in the event of network instability or communication interruption with headquarters. Configure a multi-level fault tolerance mechanism to ensure that the system can maintain basic functionality under various harsh environments.

[0038] In addition, resource management strategies for edge nodes need to be configured to enable dynamic allocation of computing resources, allowing the system to flexibly adjust resource allocation based on the priority and complexity of the current monitoring tasks, ensuring that critical security monitoring tasks receive sufficient computing resources.

[0039] II. Data Acquisition and Preprocessing (a) Data collection Video data acquisition High-definition cameras are installed in key areas of the mine, such as the coal transport system, vertical shaft hoisting system, auxiliary transport system, and tunneling faces, to ensure coverage of all areas requiring monitoring. The cameras acquire video data in real time and transmit it to edge inference devices or the mine's server. The cameras support multispectral imaging, including visible light and infrared spectra, to cope with different lighting conditions and environmental challenges. The system is also equipped with intelligent exposure control, which automatically adjusts imaging parameters to ensure high-quality images are acquired even in harsh environments such as high dust and low light.

[0040] Sensor data acquisition Various sensors are installed, such as environmental sensors (monitoring temperature, humidity, methane concentration, etc.) and equipment status sensors (monitoring equipment operating status, vibration, current, etc.), to collect relevant environmental and equipment data in real time and transmit it to edge inference devices or mine-side servers. The sensors adopt an industrial-grade design, featuring dustproof, waterproof, and shock-resistant characteristics to meet the requirements of the mining environment. Simultaneously, the sensor network deployment employs a redundant design to ensure that a single point of failure will not lead to the complete failure of the monitoring system.

[0041] (ii) Data preprocessing Video data preprocessing The system decodes, crops, and scales the acquired video data, converting it into a format suitable for model input. Video denoising and enhancement techniques are employed to improve video quality and clarity. An adaptive image correction function is implemented, automatically detecting and correcting image distortion caused by camera angle and position shifts, ensuring image data consistency. Furthermore, the system integrates advanced illumination compensation algorithms to solve image quality problems caused by uneven lighting in mining environments.

[0042] Sensor data preprocessing The system filters and normalizes sensor data to remove noise and outliers, converting the data into a unified format and range for subsequent analysis and processing. It implements multi-sensor data fusion preprocessing, automatically identifying data characteristics from different sensors and performing time synchronization and spatial registration, laying the foundation for subsequent multimodal analysis. The system also has the capability to handle missing sensor data, using interpolation and prediction methods to compensate for data loss caused by temporary sensor malfunctions.

[0043] III. Model Training and Optimization (a) Model Training Data labeling Data annotation tools are used to label the collected video and image data, including information such as human behavior, object type, and equipment status. An annotated dataset is established to provide supervised learning samples for model training. The system employs a semi-automated annotation process, generating initial annotations through a pre-trained model, which are then verified and corrected by professionals, significantly improving annotation efficiency. A quality assurance mechanism is also incorporated into the annotation process, with each annotated sample being independently checked by multiple people to ensure accuracy.

[0044] Model selection and training Depending on the application scenario and task requirements, appropriate base models are selected, such as object detection models, semantic segmentation models, and pose estimation models. Model training is performed on the AI-enabled platform (ModelArts), using labeled datasets for supervised learning to adjust model parameters and hyperparameters, thereby improving model accuracy and performance. The system supports multiple deep learning frameworks, such as TensorFlow and PyTorch, and a series of dedicated neural network structures and loss functions have been developed specifically for identifying unsafe behaviors in mines, effectively enhancing the model's adaptability to specific mining environments.

[0045] Distributed training For large-scale datasets and complex models, high-performance distributed training techniques are employed to fully utilize the computing resources of the training server and accelerate model training. The system implements a parameter server architecture and gradient synchronization mechanism to ensure the convergence and efficiency of distributed training. Simultaneously, dynamic batch size adjustment and learning rate scheduling strategies are introduced to further optimize the training process, shortening training time while improving model accuracy.

[0046] (II) Model Optimization Incremental training As new data is continuously collected and labeled, the model is incrementally trained periodically, enabling it to learn new features and patterns, thus improving its adaptability and accuracy. The system employs an automated incremental training process that automatically adjusts the training strategy based on the characteristics of new data, avoiding catastrophic forgetting. Furthermore, a knowledge distillation mechanism is implemented, allowing new models to inherit valuable feature representations from older models, accelerating convergence and improving generalization ability.

[0047] Model evaluation and tuning The trained model is evaluated using a test dataset, calculating metrics such as accuracy, recall, and F1 score. Based on the evaluation results, the model is fine-tuned, including adjustments to its structure, parameters, and hyperparameters. The system integrates advanced interpretability analysis tools, enabling visualization of the model's decision-making process and helping engineers understand its behavior and optimize it accordingly. Furthermore, an adversarial example testing mechanism is introduced, constructing boundary condition data to evaluate the model's robustness and ensure the system's reliability in real-world applications.

[0048] IV. System Operation and Monitoring (I) System Operation Data transmission and processing Edge inference devices collect real-time video and sensor data from the site and transmit it to the mine's AI application platform or the headquarters' training center system. The mine's AI application platform performs preliminary processing and analysis of the data. For simple tasks, it can directly make decisions and control; for complex tasks, the data is uploaded to headquarters for further analysis and processing. The system adopts a layered data processing architecture, dynamically determining the location of data processing based on task priority and computational complexity, optimizing network bandwidth utilization while ensuring real-time performance. Data transmission employs end-to-end encryption and compression technologies to ensure data transmission security and efficiency.

[0049] AI identification and early warning The AI application platform at the headquarters uses the trained model to perform real-time recognition and analysis on the uploaded data to determine whether there are unsafe behaviors or potential hazards. If unsafe behaviors or potential hazards are detected, the system immediately issues warning messages, including audible and visual alarms, SMS notifications, email reminders, etc. The system introduces a multi-model integrated recognition strategy to improve the recognition accuracy through the complementarity of different models, and adopts a confidence threshold dynamic adjustment mechanism to automatically optimize the alarm threshold according to the current scenario and reduce the false alarm rate. The system also has the ability of situation awareness, which can comprehensively analyze multiple relevant factors and conduct a more comprehensive assessment of potential risks.

[0050] Interlock control The system realizes the interlock with the emergency broadcast system, personnel positioning system, video surveillance system and devices. When unsafe behaviors or potential hazards are detected, the system automatically pushes information, broadcasts voice, sends instructions or controls the device to stop according to the alarm type, and the interlock response time is no more than 1 second. The system adopts a distributed decision-making mechanism, and the edge nodes can make basic safety decisions independently, and can execute key interlock controls even in the case of network interruption. The system also has an adaptive response strategy, which can automatically adjust the scope and intensity of the interlock control according to the risk level and urgency to achieve precise intervention.

[0051] (II) System monitoring Device monitoring Real-time monitoring is carried out on servers, network devices, edge inference devices, etc., and the operating status and performance indicators of the devices (such as CPU usage, memory usage, network bandwidth, etc.) are monitored to timely detect device failures and abnormal situations, and warnings and processing are carried out. The system realizes an automated health check function, comprehensively diagnoses each hardware device regularly, and discovers potential problems in advance. A device life prediction model is introduced to predict the failure risk of the device based on historical operation data, realize predictive maintenance, and reduce the unplanned downtime of the device.

[0052] Data monitoring Monitor the collected data and processing results, and check the integrity, accuracy and timeliness of the data. Monitor the transmission and storage of the data to ensure the security and reliability of the data. The system develops a data quality assessment framework to evaluate the effectiveness and representativeness of the data in real time, and automatically issues an alarm when the data quality deteriorates. The full-link tracking function of the data flow is realized, which can track the complete path of each piece of data from collection to processing, facilitating problem troubleshooting and system performance optimization.

[0053] Model monitoring The system monitors the model's operational status and performance, tracking changes in metrics such as accuracy and recall. It promptly detects model drift and failures, and updates and optimizes the model accordingly. A real-time model performance monitoring framework is deployed, continuously evaluating the model's performance in the production environment through sampling validation. A concept drift detection algorithm is introduced to automatically identify model performance degradation caused by changes in data distribution, triggering a timely model retraining process. The system also maintains model version control and a rollback mechanism to ensure rapid recovery to a stable version in the event of a model update failure.

[0054] V. System Maintenance and Upgrades (a) System Maintenance Hardware maintenance Regular hardware checks and maintenance are performed on servers, network equipment, and edge inference devices, including cleaning, replacing faulty components, and checking power supplies and network connections. The system has established detailed hardware maintenance plans and standard operating procedures to ensure the standardization and effectiveness of maintenance work. Remote diagnostic technology has been introduced, allowing technicians to remotely access equipment for fault diagnosis and maintenance guidance, reducing the need for and cost of on-site maintenance.

[0055] Software maintenance Regular maintenance is performed on software systems such as the cloud platform, edge inference platform, AI enabling platform, large-scale vision model for mines, and AI application platform, including system updates, vulnerability patching, and performance optimization. An automated software patch management mechanism has been established, capable of automatically detecting, testing, and deploying critical security patches to ensure system security. Performance monitoring and optimization tools have been developed to continuously analyze system performance bottlenecks and provide targeted optimization suggestions.

[0056] Data maintenance Regularly back up and clean the collected data to ensure its security and availability. Manage and maintain the labeled dataset to guarantee its accuracy and consistency. The system implements an automated data archiving strategy, storing data in tiers based on its value and usage frequency to optimize storage costs. A data quality assessment framework has been established to regularly audit historical data, identifying and correcting incorrectly labeled or low-quality data.

[0057] (II) System Upgrade Feature upgrade Based on user needs and business development, the system's functionality is upgraded and expanded, such as adding new recognition scenarios and optimizing linkage control functions. The system adopts a modular design and microservice architecture, supporting flexible functional expansion and on-demand deployment. A detailed functional requirements collection and evaluation process has been established to ensure that upgrade development meets actual business needs and priorities.

[0058] Model upgrade With technological advancements and data accumulation, models are upgraded and optimized to improve their accuracy and performance. New algorithms and technologies are employed to continuously refine the model's structure and parameters. The system implements an automatic model evaluation and upgrade mechanism, automatically comparing the performance of old and new models and smoothly switching between them when conditions are met. A model knowledge base has been established to record best practices and optimization experiences in different scenarios, accelerating subsequent model iteration and improvement.

[0059] Hardware upgrade Based on system operation and performance requirements, hardware such as servers and network equipment are upgraded and expanded to improve system processing capacity and reliability. A hardware resource prediction model is established to forecast future resource needs based on business growth trends, guiding hardware upgrade planning. Compatibility assessment tools are used to evaluate the compatibility of new hardware with the existing system before upgrades, minimizing upgrade risks.

[0060] VI. Empowering New Scenarios and Expanding Intelligent Applications (I) New Scene Identification and Assessment New Scene Recognition Research The system regularly analyzes accident statistics and safety production reports in the mine environment to identify new safety risks and behavioral patterns. It establishes a safety risk knowledge graph to achieve systematic management and analysis of various mine safety risks, providing a knowledge foundation for identifying new scenarios. Using unsupervised learning techniques, it discovers abnormal patterns from large amounts of unlabeled data and uncovers potential new unsafe behaviors.

[0061] Scenario Feasibility Assessment By systematically analyzing new scenarios, the technical feasibility and necessity of implementing intelligent recognition are assessed. The assessment includes multiple dimensions such as data availability, recognition technology maturity, security risk level, and the urgency of business needs. A scientific decision matrix method is used to quantitatively score the new scenarios and determine development priorities. Multidisciplinary experts are involved in the assessment process to ensure the scientific rigor and comprehensiveness of the results.

[0062] (II) Algorithm Model Development and Iteration Basic model extension Based on the characteristics and requirements of the new scenario, the basic model library will be expanded, incorporating the latest computer vision and multimodal perception algorithms. Existing basic models for object detection, behavior recognition, and semantic segmentation will be specifically optimized to improve their adaptability to the specific environment of a mine. Dedicated pre-trained models will be developed to better understand the semantics of objects, behaviors, and scenes in the mine environment.

[0063] Iterative optimization process Establish a standardized model iteration and optimization process, including problem analysis, solution design, model training, performance evaluation, and deployment verification. Employ A / B testing to run both old and new models simultaneously in the production environment and compare their performance, ensuring the effectiveness of model updates. Introduce continuous integration and continuous deployment tools to automate model testing and deployment, shortening the model's development-to-production cycle.

[0064] (III) Knowledge Transfer and Intelligent Evolution Model knowledge sharing Establish a model knowledge base to systematically record best practices and lessons learned in different scenarios. Develop model knowledge distillation technology to enable new models to learn valuable feature representations and decision logic from existing models. Through knowledge sharing mechanisms, promote complementarity and collaboration between models in different scenarios to improve the overall system's intelligence level.

[0065] Continuous learning mechanism The system possesses continuous learning capabilities, accumulating experience and optimizing decision-making logic from daily operational data. It implements a weakly supervised learning mechanism, using limited manually labeled data to guide the learning process on a large amount of unlabeled data. An adaptive learning rate strategy has been developed, enabling the system to automatically adjust learning parameters based on data characteristics and learning progress, achieving more efficient knowledge acquisition.

[0066] VII. System Security Assurance (a) Hardware network security Network security domain division Within the private cloud network, different security domains are defined, such as the DMZ zone, core business zone, and data storage zone. Isolation and access control are implemented across these different security domains to ensure network security. Traffic monitoring and anomaly detection between security domains are implemented, enabling real-time detection and blocking of abnormal cross-domain access. Micro-segmentation technology is employed to further refine the granularity of network isolation, reducing the impact of security incidents.

[0067] Firewall Configuration Deploy a new generation firewall at the network boundary, configure access policies, and restrict external network access to the internal network. Perform separate security configurations on the front-end components of the DMZ zone to ensure their security. Implement application-layer firewall functionality, capable of identifying and filtering malicious traffic for specific application protocols. Configure traffic behavior analysis capabilities to detect abnormal communication patterns by establishing a baseline of normal traffic.

[0068] (ii) VPC service security Security isolation configuration Configure multi-layered security isolation policies for VPC services to ensure network isolation between different VPCs. Properly divide and manage subnets, and set access permissions for different subnets. Implement a secure interconnection mechanism between VPCs, maintaining security boundaries while ensuring necessary communication. Establish a VPC resource access auditing system to record all cross-VPC resource access activities.

[0069] Port QoS configuration Configure fine-grained QoS policies for VPC service ports to ensure network bandwidth and service quality for critical services. Implement an adaptive bandwidth allocation mechanism that dynamically adjusts bandwidth allocation based on service priority and network conditions. Develop a network congestion prediction model to proactively adjust traffic paths before network congestion occurs, preventing service quality degradation.

[0070] (III) Security Group Service and Network ACL Service Security Group Policy Configuration Configure multi-level security group policies for elastic cloud servers to restrict access to the servers. Set default and custom security groups to ensure server security. Implement behavior-based automatic optimization of security group policies; the system can analyze historical access patterns and propose policy optimization suggestions. Develop a security group redundancy detection tool to identify and clean up redundant or conflicting security rules.

[0071] Network ACL policy configuration Configure granular network ACL policies for subnets to filter and control incoming and outgoing traffic, adding a layer of network security. Implement automatic ACL rule generation, automatically recommending appropriate ACL rules based on business needs and security best practices. Establish an ACL rule testing framework to assess the impact of rules on normal business operations before application.

[0072] (iv) Host Security Services Intrusion prevention and virus detection Deploy a new generation of endpoint protection system on the host machine to achieve integrated protection of intrusion prevention, virus scanning and removal, and abnormal behavior monitoring. The system adopts an AI-driven threat detection engine that can identify unknown malware and advanced persistent threats. It implements endpoint security situational awareness, providing a visual display and analysis of the overall endpoint security status.

[0073] Firewall and hardening Configure personalized host firewall rules to restrict access to the host. Employ system baseline hardening technology to comprehensively harden the operating system according to security baseline standards. Implement automatic host configuration compliance checks to periodically assess whether host configurations meet security policy requirements. Develop a proactive host security vulnerability scanning tool to promptly identify and patch system vulnerabilities.

[0074] (v) Data security Access control and encryption A multi-factor data access control mechanism is implemented, combining role, attribute, and context information to control data access permissions. Sensitive data undergoes full lifecycle encryption protection, including encryption during transmission, storage, and processing. Fine-grained data anonymization technology has been developed to dynamically adjust data display content based on user permissions. Data classification and hierarchical management is implemented, applying differentiated protection strategies to data with different sensitivity levels.

[0075] Log auditing and WORM settings Establish a comprehensive data operation auditing system to record all data access and modification activities. Configure a WORM (Write-once, Read-many) storage policy to prevent critical data from being tampered with. Implement a centralized log management platform that supports cross-system log correlation analysis and anomaly detection. Develop an audit log visualization tool to help security administrators quickly identify abnormal operation patterns.

[0076] (vi) Password control and system log supervision Password policy configuration Strict password control policies are implemented, including password complexity requirements, a regular password change mechanism, and historical password checks. A two-factor authentication mechanism is introduced to improve account access security. A password strength assessment tool has been developed to help users create secure and easy-to-remember passwords. Access control and session monitoring for privileged accounts are implemented to strengthen the management of high-privilege accounts.

[0077] System log analysis Deploy an advanced log analysis platform for real-time monitoring and intelligent analysis of system logs. Employ machine learning techniques to identify abnormal patterns in logs and promptly detect potential security threats. Implement log correlation analysis capabilities to identify complex attack chains from logs of different systems. Establish a long-term log archiving mechanism to meet security audit and compliance requirements.

[0078] (vii) Data update security and prevention of data theft and tampering Data backup and recovery Establish a multi-tiered data backup strategy, including full backups, incremental backups, and differential backups. Utilize DWS snapshot functionality to create point-in-time copies of data, ensuring rapid recovery in the event of data corruption or accidental deletion. Implement off-site backup functionality, storing critical data backups in physically isolated locations to prevent data loss due to catastrophic events. Develop backup verification tools to regularly test the availability and integrity of backup data.

[0079] Sensitive Data Protection Sensitive data and passwords are protected with strong encryption using industry-leading encryption algorithms and key management schemes. Access permissions for files are restricted, enabling role-based fine-grained access control. Data watermarking technology is employed to embed invisible identifying information into data for tracking data flow and tracing leaks. A digital signature mechanism is implemented to ensure data authenticity and integrity, preventing data tampering.

[0080] Example 1: Intelligent detection of personnel intrusion during coal mining machine operation Scene description: The working environment of a fully mechanized mining face is complex, and there are significant safety risks during the operation of the coal mining machine, making entry by unauthorized personnel strictly prohibited. Because the working face is monitored using a pan-tilt-zoom (PTZ) camera, the position of the coal mining machine within the monitoring screen is not fixed, making traditional fixed-area monitoring methods unsuitable for the dynamic working environment. On-site personnel need to coordinate operations while ensuring safety, but manual supervision suffers from blind spots and delayed response times, making it impossible to effectively prevent personnel from accidentally entering dangerous areas at all times.

[0081] Implementation path: A PTZ camera is installed at the fully mechanized mining face, and AI technology is used to distinguish between two operating states: with and without a coal mining machine, so as to realize the dynamic drawing of an intelligent electronic fence.

[0082] The system first uses machine vision to identify the presence and operating status of the coal mining machine. When the presence of the coal mining machine or coal cutter is detected, a full-area electronic fence is generated for monitoring personnel intrusion into dangerous areas of the coal face. When there is no coal mining machine, the system switches to static electronic fence mode to monitor personnel intrusion into fixed dangerous areas of the working face.

[0083] By integrating two monitoring modes, the system achieves comprehensive safety supervision of the working face. When personnel are detected entering the dynamic or static electronic fence, the system immediately triggers an alarm, issues a voice warning through the on-site broadcast system, and simultaneously uploads alarm data containing information such as personnel location and coal mining machine status to the mine-side platform. If necessary, the system can trigger an emergency shutdown of the coal mining machine.

[0084] Example 2: Monitoring of Unauthorized Operations in Operating Parts of Equipment Scene description: When various types of mechanical equipment are in operation underground, there are significant safety hazards around their transmission parts and rotating components. Personnel carrying tools or body parts that accidentally enter the operating area can easily cause serious injuries or fatalities. Traditional supervision methods mainly rely on on-site safety officer inspections and workers' voluntary adherence to safety procedures. However, the operating status of equipment changes frequently, making real-time, comprehensive manual supervision difficult and prone to misjudgments and omissions.

[0085] Implementation path: High-definition cameras are installed in the equipment operating area to simultaneously identify equipment operating status and detect personnel violations through a single camera.

[0086] The system first establishes a device operation status recognition model. By analyzing the motion characteristics, sound spectrum, and vibration patterns of key components, it accurately determines the device's start-up and shutdown status and operating parameters. When the system detects that the device has entered an operating state, it automatically activates the personnel intrusion detection function as a trigger signal for AI recognition.

[0087] Based on electronic fence technology, the system focuses on monitoring whether a person's torso and tools they are carrying enter the equipment's hazardous area. Through precise human key point detection and object recognition algorithms, the system can distinguish different body parts and tool types, enabling targeted safety monitoring. When a person's torso or tools are detected entering the electronic fence, the system immediately issues an alert, significantly improving safety supervision and effectively reducing false alarm rates.

[0088] Example 3: Monitoring of Pedestrian and Vehicle Movement in Assisted Transportation Scene description: In underground auxiliary transport roadways, the mixing of vehicles and pedestrians poses a significant safety hazard, especially in areas with poor visibility such as turns and slopes. Current management regulations stipulate that vehicles should not be used for pedestrian traffic, but in practice, violations frequently occur where people are still walking in the roadways while vehicles are in motion. Traditional supervision relies on manual patrols and driver observation, which has blind spots and delayed response times.

[0089] Implementation path: Intelligent camera equipment is installed at key locations in auxiliary transportation lanes to achieve collaborative monitoring of vehicle operation status and personnel behavior through AI technology.

[0090] The system establishes an intelligent algorithm for determining vehicle direction of travel. By analyzing the vehicle's trajectory, velocity vector, and heading, it accurately identifies the vehicle's direction of travel and expected path. Based on the vehicle's operating status, the system dynamically draws electronic fences for hazardous areas in front of and around the vehicle. The fence range is adjusted in real time according to vehicle speed, road conditions, and braking distance.

[0091] Meanwhile, the system integrates personnel posture recognition functionality, analyzing personnel's limb movements, walking speed, and direction of movement to determine whether personnel are engaging in risky behaviors that could conflict with vehicles. The system can recognize different postures such as standing, walking, and running, and combined with dynamic hazard zone information from vehicles, effectively identify and warn of unsafe behaviors, significantly improving the model's accuracy and practicality.

[0092] Example 4: Identification of personnel entering hazardous areas while tensioning anchor cables Scene description: During the tensioning of anchor cables, this area is considered a danger zone and personnel are prohibited from entering. Currently, due to the subjectivity and limitations of manual supervision, it is impossible to control the dangerous area on site in a timely and comprehensive manner. It is necessary to use artificial intelligence technology for real-time monitoring.

[0093] Implementation path: Cameras are installed at the anchor cable tensioning site to collect real-time video footage and analyze it using artificial intelligence technology. The system first identifies the preceding and following stages of the anchor cable tensioning operation, including different phases such as anchor cable installation preparation, tensioning equipment placement, formal tensioning operation, and operation completion.

[0094] When the system detects the entry into the tensioning and anchoring stage, the electronic fence function around the work area automatically activates, detecting whether personnel have entered the fenced area. The system can distinguish between working personnel and non-working personnel, only triggering an alarm for unauthorized personnel. When unauthorized entry is detected, timely alerts and alarms are issued, and relevant videos or images are uploaded to the application platform for timely handling and review by management personnel, improving on-site operational safety.

[0095] After the work is completed, the system automatically identifies the process transition and adjusts the monitoring strategy accordingly to achieve intelligent safety management and control of the entire process.

[0096] Example 5: Monitoring of Monorail Overhaul Procedures Scene description: Monorail maintenance involves multiple procedures, each with strict safety operating procedures. During maintenance, personnel must work at heights, using various specialized tools and equipment, making the work inherently risky. Traditional maintenance supervision relies mainly on on-site supervision by team leaders and post-construction review of maintenance records, which makes real-time, standardized monitoring of the maintenance process difficult and poses safety hazards.

[0097] Fixed cameras are installed at the monorail maintenance points to continuously capture footage of the monorail maintenance work. Based on preset maintenance action standards and clearly defined maintenance cycles and dwell time rules, the AI ​​model analyzes key data during personnel inspections in real time, including inspection cycles and dwell time in the inspected areas. If any violations such as missed inspections, failure to inspect, or insufficient inspection time are detected, the system immediately triggers an on-site voice alarm, reminding personnel to correct the issues promptly, ensuring the standardization of monorail maintenance operations and the quality of equipment maintenance. (A sign must be held up in front of the camera before maintenance; maintenance frequency is once daily, and the cumulative duration of each maintenance session must exceed one hour).

[0098] Scenario Implementation: High-definition fixed cameras are installed at appropriate locations above the fixed maintenance points of the monorail to ensure complete coverage of the refueling and maintenance work area. An AI model for standardized monitoring of monorail refueling and maintenance is constructed based on deep learning technology, mastering standard operating procedures and time parameters. Specific alarm trigger conditions are as follows: within two hours of raising the "Maintenance Start" sign, the total time spent by personnel in the maintenance area does not exceed one hour; the total time spent by personnel between raising the "Maintenance Start" sign and the "Maintenance End" sign is less than one hour; no "Maintenance Start" sign record is detected on the same day; when an alarm is triggered, a single on-site voice reminder is given, without repeated alarms, and the alarm information is simultaneously pushed to the AI ​​platform to achieve intelligent monitoring and management of monorail maintenance operations.

[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An AI-powered intelligent recognition system for unsafe behaviors in mines, characterized in that, This includes the headquarters training center system and the mining terminal system, which achieve data interaction and linkage control through cloud-edge collaboration technology; The headquarters training center system includes a training center server cluster, a network equipment cluster, a cloud platform, an edge inference platform, an AI enabling platform, a large-scale vision model for mines, and an AI application platform. The mining system includes edge inference devices for real-time data acquisition and preliminary data processing, as well as a mining AI application platform that synchronizes and communicates with the headquarters platform to enable local decision-making and control.

2. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The training center server cluster includes training servers, gateway servers, compute node servers, management node servers, and object storage servers, providing computing, storage, and network resource support for the system.

3. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The network device cluster includes AIROCE network switches, object storage switches, BMC switches, management service Tor switches, and core switches, forming a high-speed and stable network communication environment.

4. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The cloud platform consists of an infrastructure layer, a resource pool layer, a cloud service layer, a presentation layer, and a management domain, providing IaaS and PaaS services to achieve automated allocation and management of cloud resources.

5. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The AI ​​enabling platform provides end-to-end AI development and inference services, supports heterogeneous resource scheduling and management, and covers AI development, model training, inference services, multi-framework support, and unified model management functions.

6. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The large-scale vision model for the mine includes a development kit and a basic model. The basic model has AI atomic capabilities for object detection and semantic segmentation, and supports incremental training and self-iteration.

7. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The AI ​​application platform has integrated dashboard, alarm management, and video management modules, and has corresponding requirements in terms of performance, security, reliability, compatibility, ease of use, and maintainability.

8. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The system adopts a full-mesh fully connected networking approach, the server supports 100GE speed, the training server is interconnected with the existing cloud platform through a 10GE network, and the AI ​​training platform supports resume training after interruption.

9. The AI-powered intelligent identification system for unsafe behaviors in mines according to claim 1, characterized in that, The system enables real-time identification of violations and unsafe factors during coal mine production, and provides local alarms through voice cameras and emergency broadcasts. It can also link with on-site equipment for emergency shutdown.

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