Working power supply system under mine

Abstract

The invention belongs to the technical field of power supply systems, and particularly relates to an underground working power supply system, which comprises an equipment operation state monitoring subsystem for acquiring temperature, current and voltage parameters in real time through a sensor network deployed in underground key equipment, and comparing the temperature, current and voltage parameters with a preset safety threshold to generate equipment health assessment data; the worker safety monitoring subsystem is used for acquiring heart rate, body surface temperature and action posture data through wearable equipment, and calculating a comprehensive fatigue index in combination with the working duration record of the attendance system; an intelligent decision module; a power supply drill execution module; a data management module; and a man-machine interaction module. The drilling is executed in the window period with high equipment risk and the best personnel state, so that the drilling in the high-load operation period of the equipment or the fatigue period of workers is avoided, the drilling effect is ensured, and the interference of the drilling on the production rhythm is reduced to the greatest extent.

Description

Technical Field

[0001] This invention belongs to the field of power supply system technology, specifically relating to a power supply system for underground mining operations. Background Technology

[0002] In the underground mining environment, the reliability of the power supply system is directly related to the safety of miners and production efficiency. The complex underground working environment means that equipment may malfunction after prolonged operation, and workers are prone to fatigue from long hours of high-intensity work; all of these factors can lead to safety accidents. To improve the safety and reliability of underground power supply systems, traditional emergency drills typically rely on regular power outage exercises. However, this method has the following problems: Current drills often rely on fixed cycles (e.g., monthly) or manual experience-based judgment, without dynamically adjusting to real-time equipment operating status and worker workload. Drills conducted during peak worker fatigue periods lead to increased emergency operation errors due to slowed reaction times.

[0003] Traditional power outage drills require actual disconnection of power lines, leading to production equipment shutdowns and transportation system disruptions. Sudden, realistic power outage simulations can easily cause worker panic and lack adaptive protection for personnel. During drills, the interruption of lighting and ventilation systems can easily cause collisions in narrow passageways.

[0004] The exercise records and equipment operation data are stored in isolation, lacking multi-dimensional correlation analysis, making it impossible to assess the long-term impact of the exercises on equipment health, and making it difficult to provide an effective basis for optimizing the frequency and duration of exercises. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a power supply system for underground mining operations. This system can reduce the impact of drills on equipment and workers, while also lowering the error rate in emergency operations.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A power supply system for underground mining operations, comprising: The equipment operation status monitoring subsystem collects temperature, current, and voltage parameters in real time through a sensor network deployed in key underground equipment, and compares them with preset safety thresholds to generate equipment health assessment data. The worker safety monitoring subsystem acquires heart rate, body surface temperature, and movement posture data through wearable devices, and calculates the comprehensive fatigue index by combining the work time records from the attendance system. The intelligent decision-making module receives the equipment health assessment data and comprehensive fatigue index, and generates exercise decision instructions based on pre-stored safety rules and dynamic optimization strategies. The power supply drill execution module includes a control unit and a backup power supply device. The control unit controls the power supply line to switch on and off according to the drill decision instructions to simulate a power outage scenario. The backup power supply device maintains power supply to critical equipment during the power outage. The data management module stores device operation data, personnel status data, and drill records, and generates a visual analysis report that includes fault prediction and drill results. The human-computer interaction module provides a 3D mine map interface that displays equipment status, personnel location, and exercise progress in real time, and supports managers to manually adjust decision parameters.

[0007] The equipment operation status monitoring subsystem is configured to perform dynamic threshold adjustment, wherein the preset safety threshold is adaptively optimized based on the equipment's historical operating data, real-time load rate, and environmental factors.

[0008] The worker safety monitoring subsystem is configured to identify the deviation of personnel behavior patterns from standard operating procedures based on the action posture data and working time records, and to use this deviation as a weighting factor in the calculation of the comprehensive fatigue index.

[0009] The intelligent decision-making module also includes a multi-objective optimization unit, which is used to simultaneously optimize equipment safety, personnel fatigue status and production continuity objectives when generating exercise decision instructions, and output a Pareto optimal solution set for decision-making reference.

[0010] The power supply drill execution module is configured to support a tiered drill mode, and can selectively cut off non-critical loads or enter a regional rotation power outage mode according to the risk level indicated by the drill decision instruction, so as to minimize the impact of the drill on production.

[0011] The data management module also includes a causal analysis engine, which is used to construct a directed graph model of fault propagation based on the exercise records and equipment operation data, and to identify key equipment nodes and potential cascading failure paths.

[0012] The human-computer interaction module also includes a collaborative decision dashboard, which is used to summarize and visualize the evaluation results and decision suggestions of each subsystem before the exercise starts, and record the reasons for manual adjustments by managers to form a decision log for subsequent strategy optimization.

[0013] It also includes a post-exercise evaluation module, which compares the personnel response data and equipment status data in the simulated power outage scenario with those in historical real-world fault events, quantitatively evaluates the effectiveness of the exercise plan, and automatically generates calibration parameters.

[0014] Compared with the prior art, the beneficial effects of this invention are: Traditional drills, while notified in advance, are conducted on a fixed schedule (e.g., monthly), making it impossible to dynamically match with real-time equipment status and personnel workload. This invention, through an equipment operation status monitoring subsystem and a worker safety monitoring subsystem, integrates real-time data on equipment health and worker fatigue, with an intelligent decision-making module dynamically generating drill decisions. This allows drills to be performed during windows of opportunity when equipment risk is high and personnel are in optimal condition, avoiding drills during periods of high equipment load or worker fatigue. This ensures the effectiveness of the drills while minimizing disruption to production schedules.

[0015] Traditional drills, even with advance notice, still require actual power outages, leading to equipment shutdowns and transportation disruptions. This system's power supply drill execution module supports a tiered drill mode, intelligently selecting to cut off only non-critical loads or rotate power outages across different areas based on risk level. This ensures drill effectiveness while maximizing the continuous operation of the main production system. Simultaneously, backup power supplies continuously power critical safety equipment such as lighting and ventilation during drills, ensuring a safe drill environment and eliminating potential risks.

[0016] Traditional exercise effectiveness evaluations often rely on subjective experience and lack data support. This invention uses a data management module to centrally store multi-dimensional data on equipment, personnel, and the exercise process. Its causal analysis engine deeply mines data correlations and quantifies the long-term impact of the exercise on equipment operation and personnel behavior. The post-exercise evaluation module compares response data from simulated exercises with those from real events to scientifically assess the effectiveness of the exercise plan and automatically generate calibration parameters. This provides precise data for optimizing exercise strategies, enabling continuous improvement of emergency management capabilities.

[0017] This invention systematically solves the three core pain points of traditional drills—fixed timing, impact on production, and subjective assessment—through intelligent and data-driven methods, and constructs a new paradigm for efficient, intelligent, and continuously evolving mine power supply safety management. Attached Figure Description

[0018] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] This invention provides a power supply system for underground mining operations, which aims to improve the inherent safety level of mine production by achieving efficient management and emergency drills for underground power supply safety through highly integrated intelligent monitoring and decision-making.

[0021] like Figure 1As shown, the system includes: an equipment operation status monitoring subsystem, a worker safety monitoring subsystem, an intelligent decision-making module, a power supply drill execution module, a data management module, and a human-computer interaction module.

[0022] The equipment operation status monitoring subsystem collects operating parameters in real time through a network of various sensors deployed on key underground equipment (such as coal mining machines, ventilation fans, water pumps, and power distribution switches). These sensors include, but are not limited to, temperature sensors, current transformers (CTs), and voltage transformers (PTs). The collected raw data is transmitted to the data processing unit via an underground industrial ring network or wireless sensor network.

[0023] This subsystem has a built-in threshold comparison algorithm that compares real-time data with preset safety thresholds. For example, when the motor bearing temperature exceeds 85°C or the operating current exceeds 115% of the rated value for 5 consecutive minutes, it will generate "early warning" level equipment health assessment data; if the parameters deteriorate further, it will generate "alarm" level data.

[0024] Building upon this, the subsystem is further configured to perform dynamic threshold adjustments. The preset safety thresholds are not fixed values, but rather adaptively optimized based on historical equipment operating data (such as normal parameter ranges during break-in, stabilization, and aging periods), real-time load rates (such as temperature rise characteristics under light and heavy loads), and environmental factors (such as mine depth and roadway humidity). For example, machine learning algorithms (such as time series analysis) are used to learn the normal operating modes of the equipment under specific environments, dynamically adjusting alarm thresholds to reduce false alarms and missed alarms.

[0025] The worker safety monitoring subsystem acquires physiological and behavioral data, including heart rate, body surface temperature, and motion posture data based on a nine-axis inertial measurement unit (IMU), through smart wearable devices worn by miners (such as smart bracelets and helmet-integrated sensors). Simultaneously, the system interfaces with the mine's personnel positioning and attendance system to obtain each worker's real-time location, underground working hours, and labor intensity information. The core processing unit calculates the worker's comprehensive fatigue index based on predefined algorithm models (such as fatigue models based on heart rate and body surface temperature).

[0026] Furthermore, this subsystem analyzes motion and posture data (such as limb swing amplitude, body balance, and the standardization of operational movements) and work duration records to identify the deviation of an employee's current behavioral pattern from the standard operating procedures (SOP). This deviation (such as movement distortion or slow reaction time) is used as an important weighting factor in the calculation of the overall fatigue index. For example, when a worker's movement deviation is detected to be continuously increasing, even if their heart rate data does not show obvious abnormalities, their overall fatigue index will be adjusted accordingly to more accurately reflect their true condition.

[0027] The intelligent decision-making module receives equipment health assessment data from the equipment operation status monitoring subsystem and a comprehensive fatigue index from the worker safety monitoring subsystem. Internally, the module stores a safety rule base containing various scenarios (e.g., "If the health of critical equipment drops to 'dangerous' and there are personnel with a 'high' fatigue index in the area, immediately initiate an emergency drill") and a set of dynamic optimization strategies (including weight allocation and target priority ranking). Based on these rules and strategies, the module generates specific drill decision instructions, such as "Initiate a Level 1 drill in mining area No. 3."

[0028] More preferably, this module also includes a multi-objective optimization unit. When generating exercise decision instructions, this unit simultaneously considers multiple potentially conflicting objectives, such as equipment safety (avoiding equipment damage), personnel fatigue (ensuring personnel safety), and production continuity (minimizing output loss). By employing a multi-objective optimization algorithm (such as the non-dominated sorting genetic algorithm NSGA-II), this unit can output a Pareto optimal solution set (i.e., a set of optimal trade-offs), for example: "Solution A: Small power outage area, 20% reduction in personnel risk, 5% output loss; Solution B: Large power outage area, 50% reduction in personnel risk, 15% output loss." This solution set is provided to managers for final decision-making reference through the human-computer interaction module.

[0029] The power supply drill execution module is responsible for receiving and executing drill decision instructions issued by the intelligent decision module. It includes a highly reliable control unit (such as a PLC or intelligent circuit breaker controller) and a backup power supply unit (such as a UPS or emergency diesel generator). The control unit precisely controls the on / off switching of the underground power supply lines according to the instructions to simulate real power outage scenarios. Simultaneously, the backup power supply unit is immediately activated, providing temporary power to critical underground safety equipment (such as emergency lighting, ventilation equipment, and communication base stations) to ensure basic safety during the drill.

[0030] This module is configured to support a tiered drill mode. The drill decision instructions include a risk assessment-based level designation. The module can select different drill strategies based on different levels: for low-risk drills, it may only shut down some non-critical loads (such as some lighting); for high-risk alerts, it may initiate a regional rotation power outage mode, simulating short-term power outages in different areas sequentially, thereby comprehensively testing the system's response capabilities while minimizing the impact on production.

[0031] The data management module, serving as the system's data hub, is responsible for storing all historical and real-time data, including equipment operation data, personnel status data, and a complete record of each exercise (such as instruction content, execution results, and response time). This module employs a combination of time-series and relational databases to efficiently manage massive amounts of data.

[0032] In addition, this module integrates a causal analysis engine. Based on accumulated exercise records and equipment operation data, this engine uses graph computing and probabilistic reasoning methods to construct a directed graph model of fault propagation in the mine power supply system. This model can clearly show other equipment nodes that may be affected by a chain reaction after a failure of a certain equipment, thereby identifying critical equipment nodes and potential cascading failure paths in the system, providing data support for proactive maintenance and system hardening.

[0033] Ultimately, the module can automatically generate visual analysis reports, which cover equipment failure prediction trends, comparisons of the effects of previous drills (such as improved personnel evacuation efficiency and shortened emergency response time), and provide intuitive basis for management decisions.

[0034] The human-computer interaction module provides an operating interface for managers at the ground dispatch center. At its core is a 3D mine map interface rendered based on technologies such as WebGL, which can display the operating status of underground equipment in real time and realistically (such as color differentiation of health, warning, and alarm), the precise location and dynamic trajectory of personnel, and the progress of drills (such as flashing of power outage areas and marking of power restoration areas).

[0035] This module also provides a collaborative decision-making dashboard. Before each exercise begins, the dashboard summarizes and visualizes the evaluation results of each subsystem (equipment monitoring, personnel monitoring) and the decision suggestions (including Pareto optimal solutions) provided by the intelligent decision-making module. Managers can conduct discussions on this interface and have the final decision-making authority, manually adjusting decision parameters (such as adjusting the exercise level or modifying the power outage area). The system fully records all manual adjustments and their rationale, forming a decision log. These logs are used for subsequent iterative optimization of the intelligent decision-making strategy.

[0036] The post-exercise evaluation module is a crucial component for the system's self-evolution. After each exercise, this module compares and analyzes personnel response data (such as evacuation speed and operational compliance) and equipment status data (such as backup power switching time and equipment restart performance) collected during the simulated power outage scenario with data records from historical real-world failure events. It quantifies the effectiveness of the exercise plan by calculating key indicators (such as response time difference and success rate improvement percentage). Based on the evaluation results, the module automatically generates parameter suggestions for calibrating rules and strategies in the intelligent decision-making module (such as adjusting the weight of the fatigue index and optimizing power outage duration), thereby achieving closed-loop management of exercise effectiveness and continuous system improvement.

[0037] The system provided in this invention, through the collaborative work of the above subsystems, realizes intelligent management of the entire process of "monitoring-assessment-decision-drill-analysis-optimization" for mine power supply safety, significantly improving the mine's emergency response capability and safety production level.

[0038] The above description only illustrates preferred embodiments of the present invention, but the present invention is not limited to the above embodiments.

Claims

1. A power supply system for underground mining operations, characterized in that, include: The equipment operation status monitoring subsystem collects temperature, current, and voltage parameters in real time through a sensor network deployed in key underground equipment, and compares them with preset safety thresholds to generate equipment health assessment data. The worker safety monitoring subsystem acquires heart rate, body surface temperature, and movement posture data through wearable devices, and calculates the comprehensive fatigue index by combining the work time records from the attendance system. The intelligent decision-making module receives the equipment health assessment data and comprehensive fatigue index, and generates exercise decision instructions based on pre-stored safety rules and dynamic optimization strategies. The power supply drill execution module includes a control unit and a backup power supply device. The control unit controls the power supply line to switch on and off according to the drill decision instructions to simulate a power outage scenario. The backup power supply device maintains power supply to critical equipment during the power outage. The data management module stores device operation data, personnel status data, and drill records, and generates a visual analysis report that includes fault prediction and drill results. The human-computer interaction module provides a 3D mine map interface that displays equipment status, personnel location, and exercise progress in real time, and supports managers to manually adjust decision parameters.

2. The underground power supply system according to claim 1, characterized in that: The equipment operation status monitoring subsystem is configured to perform dynamic threshold adjustment, wherein the preset safety threshold is adaptively optimized based on the equipment's historical operating data, real-time load rate, and environmental factors.

3. The underground power supply system according to claim 1, characterized in that: The worker safety monitoring subsystem is configured to identify the deviation of personnel behavior patterns from standard operating procedures based on the action posture data and working time records, and to use this deviation as a weighting factor in the calculation of the comprehensive fatigue index.

4. A power supply system for underground mining operations according to claim 1, characterized in that: The intelligent decision-making module also includes a multi-objective optimization unit, which is used to simultaneously optimize equipment safety, personnel fatigue status and production continuity objectives when generating exercise decision instructions, and output a Pareto optimal solution set for decision-making reference.

5. A power supply system for underground mining operations according to claim 1, characterized in that: The power supply drill execution module is configured to support a tiered drill mode, and can selectively cut off non-critical loads or enter a regional rotation power outage mode according to the risk level indicated by the drill decision instruction, so as to minimize the impact of the drill on production.

6. A power supply system for underground mining operations according to claim 1, characterized in that: The data management module also includes a causal analysis engine, which is used to construct a directed graph model of fault propagation based on the exercise records and equipment operation data, and to identify key equipment nodes and potential cascading failure paths.

7. A power supply system for underground mining operations according to claim 1, characterized in that: The human-computer interaction module also includes a collaborative decision dashboard, which is used to summarize and visualize the evaluation results and decision suggestions of each subsystem before the exercise starts, and record the reasons for manual adjustments by managers to form a decision log for subsequent strategy optimization.

8. A power supply system for underground mining operations according to claim 1, characterized in that: It also includes a post-exercise evaluation module, which compares the personnel response data and equipment status data in the simulated power outage scenario with those in historical real-world fault events, quantitatively evaluates the effectiveness of the exercise plan, and automatically generates calibration parameters.

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