Coal mine power supply management monitoring and early warning system

Abstract

The invention belongs to the technical field of coal mine power supply systems, and particularly relates to a coal mine power supply management monitoring and early warning system which comprises a multi-mode sensing terminal, a two-channel redundant communication network, an edge computing gateway and a cloud platform decision center. According to the system, omnibearing and multi-dimensional monitoring of the coal mine power supply system is realized through the multi-mode sensing terminal, accurate acquisition of information such as electrical parameters, equipment operation states and environment gas is realized, and a rich data basis is provided for subsequent safety analysis and energy consumption management. Through cooperative work of the edge computing gateway and the cloud platform decision center, the system can quickly process mass data and realize intelligent functions of equipment health management, dynamic trough scheduling and the like, so that the reliability and the stability of equipment operation are effectively improved, the service life of equipment is prolonged, energy consumption distribution can be optimized according to real-time electricity price and load conditions, and the energy efficiency is improved. The coal mine electricity consumption cost is reduced, and the purposes of energy conservation and consumption reduction are

Description

Technical Field

[0001] This invention belongs to the technical field of coal mine power supply systems, specifically relating to a coal mine power supply management, monitoring and early warning system. Background Technology

[0002] As a critical energy source for underground production operations, coal mine power supply systems have long faced numerous challenges in ensuring power supply security and implementing energy efficiency management. Traditional methods mainly focus on monitoring electrical parameters, such as voltage and current, while neglecting the coordinated monitoring of multi-dimensional status information such as mechanical vibration, gas concentration, and temperature rise.

[0003] Current energy optimization systems are mostly based on time-of-use pricing for load scheduling, but they often overlook equipment health status and environmental safety constraints. There have been cases where mines excessively increased the load on drainage pumps during off-peak electricity periods, causing the motor insulation to overheat and break down, which in turn led to secondary methane explosions. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a coal mine power supply management monitoring and early warning system, which aims to achieve precise energy consumption control while ensuring power supply safety.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A coal mine power supply management monitoring and early warning system, comprising: Multimodal sensing terminals are deployed in ground substations, underground substations, and key electrical equipment, including transient waveform voltage and current sensors, high-temperature resistant wireless vibration sensors, methane sensors, and infrared carbon monoxide and temperature dual-mode detectors. A dual-channel redundant communication network, comprising a backbone fiber optic ring network and an emergency channel based on long-distance wireless communication; Edge computing gateways are used for data preprocessing and rapid local control response; The cloud platform decision-making hub integrates an equipment health management platform, a dynamic valley scheduling engine, a 3D digital twin module, a blockchain evidence storage module, an early warning unit, and an energy consumption verification module.

[0006] The device health management platform includes: The health factor calculation unit is used to calculate the health of the equipment based on temperature, humidity and dust concentration data. The health is the product of an exponential function and the humidity offset and dust concentration. The fault prediction unit is used to predict the remaining life of the equipment through a time-series prediction model. The input features include current harmonic distortion rate, vibration peak factor and health time-series data.

[0007] The dynamic valley scheduling engine includes: The input interface is used to receive real-time electricity price signals, equipment health status, and load forecast curves. The optimized processor performs energy consumption optimization calculations with safety constraints, aiming to minimize total electricity costs. The constraints include: real-time equipment temperature not exceeding a temperature threshold; equipment health not falling below a health threshold; power fluctuation rate not exceeding a rated power threshold; cutting off power to non-explosion-proof equipment when methane concentration reaches a safety threshold; and activating ventilation equipment and pausing energy consumption optimization when carbon monoxide concentration reaches a safety threshold.

[0008] The three-dimensional digital twin module includes: The virtual mapping unit is used to construct a three-dimensional model of the power supply system and map electrical parameters and equipment status in real time; The risk visualization unit is used to pinpoint the location of high-risk equipment, simulate fault protection actions, and display energy savings and safety risk distribution.

[0009] The early warning unit includes: A real-time alarm unit is used to dynamically set the overload threshold based on the temperature rise characteristics; The fusion diagnostic unit is used to perform risk fusion on current surges, vibration data and gas concentration, and triggers a shutdown command when the comprehensive risk value exceeds the threshold. The trend warning unit is used to trigger equipment failure alarms in advance based on the prediction model.

[0010] The energy consumption verification module includes: The strategy execution unit is used to control the energy storage switching of charging equipment, the start and stop regulation of drainage pumps, and the reactive power compensation of ventilation fans; The verification unit is used to calculate the deviation in electricity consumption per ton of coal and correct the scheduling parameters.

[0011] The blockchain evidence storage module includes: The instruction recording unit is used to store the hash value and timestamp of the operation instruction; The emergency response unit freezes dispatch instructions and generates tamper-proof logs when the equipment health or methane concentration reaches a dangerous threshold.

[0012] The dual-channel redundant communication network includes: The data acquisition interface adopts the industrial bus protocol; The command transmission channel is used by the cloud platform to send optimization commands to the edge gateway. The priority controller is used to set the safety interlock command as the highest transmission priority.

[0013] Compared with the prior art, the beneficial effects of this invention are: This system achieves comprehensive and multi-dimensional monitoring of the coal mine power supply system through multi-modal sensing terminals, including the accurate collection of information such as electrical parameters, equipment operating status, and ambient gas conditions, providing a rich data foundation for subsequent safety analysis and energy consumption management. A dual-channel redundant communication network ensures the stability and reliability of data transmission, avoiding monitoring blind spots caused by communication failures.

[0014] The collaborative work of edge computing gateways and cloud platform decision centers enables the system to quickly process massive amounts of data and realize intelligent functions such as equipment health management and dynamic off-peak scheduling. This not only effectively improves the reliability and stability of equipment operation and extends equipment lifespan, but also optimizes energy consumption allocation based on real-time electricity prices and load conditions, reducing electricity costs in coal mines and achieving energy conservation and consumption reduction goals.

[0015] The combination of a three-dimensional digital twin module and an early warning unit provides an intuitive view of the power supply system's operating status, accurately locates high-risk areas, and issues early warnings and risk alerts for faults, giving coal mines time to take preventative measures and effectively ensuring coal mine production safety.

[0016] The energy consumption verification module ensures the effective execution of energy consumption optimization strategies and continuously improves the scheduling scheme based on actual energy consumption data, thereby further improving energy utilization efficiency. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0018] 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.

[0019] like Figure 1 As shown, a coal mine power supply management monitoring and early warning system includes a multimodal sensing terminal, a dual-channel redundant communication network, an edge computing gateway, and a cloud platform decision center.

[0020] The multimodal sensing terminals include transient waveform voltage and current sensors, high-temperature resistant wireless vibration sensors, methane sensors, and infrared carbon monoxide and temperature dual-mode detectors deployed in ground substations, underground substations, and key electrical equipment.

[0021] Transient waveform voltage and current sensors are specifically deployed at critical nodes in ground substations, underground substations, and key electrical equipment. For example, at high-voltage switchgear, these sensors collect voltage and current data at frequencies of thousands or even higher per second, providing high-precision, high-resolution raw data for subsequent power quality analysis, fault location, and diagnosis. This ensures that subtle fluctuations and sudden changes in voltage and current can be captured, and potential power supply safety hazards can be detected in a timely manner.

[0022] High-Temperature Resistant Wireless Vibration Sensor: Designed for harsh environments such as underground coal mines, this high-temperature resistant wireless vibration sensor can be installed on the casing or bearings of critical operating equipment such as motors and transformers. It monitors the vibration status of the equipment in real time, converting the vibration signal into an electrical signal and wirelessly transmitting it to an edge computing gateway. This allows for the timely detection of potential mechanical faults, such as imbalance, misalignment, and bearing wear, providing crucial data support for preventative maintenance.

[0023] A methane sensor based on laser absorption spectroscopy and a dual-mode infrared carbon monoxide and temperature detector. The dual-mode infrared carbon monoxide and temperature detector combines the principles of infrared absorption spectroscopy with the technology of measuring temperature using a thermistor. On the one hand, it monitors the concentration of carbon monoxide gas by utilizing its absorption characteristics in a specific infrared band; on the other hand, it measures the ambient temperature in real time using a thermistor.

[0024] Dual-channel redundant communication network (backbone fiber optic ring network, emergency channel based on long-distance wireless communication) Backbone Fiber Optic Ring Network: A ring network using optical fiber as the transmission medium is constructed as the backbone communication network for the coal mine power supply safety and energy consumption reduction monitoring and early warning system. In the ring network, all nodes are connected via optical fibers, and data is transmitted bidirectionally. If a fiber optic line fails at any point, data can automatically switch to the other direction of transmission, ensuring the continuity and reliability of communication. For example, fiber optic lines are laid between the coal mine's surface substation and various underground substations and key electrical equipment, forming a closed ring network to achieve real-time and efficient data transmission, providing stable communication support for the system's centralized monitoring and management.

[0025] Emergency communication channels based on long-distance wireless communication: Considering the special circumstances such as fiber optic line failures and network congestion that may occur underground in coal mines, an emergency communication channel based on long-distance wireless communication technology is constructed, such as using radio stations or 4G / 5G private networks. Under normal circumstances, this emergency channel is in standby mode. When the backbone fiber optic ring network fails, the system automatically switches to the emergency channel for data transmission, ensuring that monitoring data and control commands can be sent and received in a timely and accurate manner. For example, wireless communication base stations are set up at regular intervals underground in the coal mine, forming a wireless communication network covering the entire mine, serving as the infrastructure for the emergency communication channel. When the fiber optic ring network is interrupted, each multimodal sensing terminal and edge computing gateway establishes a connection with the cloud platform decision-making center through the wireless communication base station to continue data interaction, ensuring the normal operation of the system and improving the system's fault tolerance and emergency response capabilities.

[0026] Edge computing gateways, as key data processing units in coal mine power supply safety and energy consumption reduction monitoring and early warning systems, are deployed near multimodal sensing terminals, such as in substation control rooms and underground substations. Their main function is to preprocess large amounts of real-time data from multimodal sensing terminals and provide rapid local control responses.

[0027] Data Preprocessing: The edge computing gateway receives raw data from various sensors, including voltage and current values, vibration signals, methane concentration, carbon monoxide concentration, and temperature. First, this data is cleaned to remove noise and outliers. For example, filtering algorithms remove high-frequency noise interference from voltage and current signals, and statistical analysis methods identify and eliminate impulse noise points in vibration signals. Then, data fusion is performed, correlating and integrating data from different sensors to generate more informative datasets. For instance, combining motor vibration and current data allows analysis of the relationship between motor operating status and load changes. Simultaneously, data compression and encoding are performed to reduce data volume, improve data transmission efficiency, and reduce bandwidth consumption on the communication network.

[0028] Local rapid control response: Based on pre-processed data, the edge computing gateway can monitor the operating status of equipment in real time and respond rapidly according to preset control strategies. For example, when the vibration signal of a motor exceeds a set threshold and abnormal current fluctuations are detected, the edge computing gateway determines that the motor may have a mechanical failure or overload. It immediately sends a shutdown command to the motor's protection device through the local control interface, cutting off the motor's power supply to prevent further escalation of the fault and to prevent equipment damage and safety accidents. In addition, the edge computing gateway can also automatically adjust the operating status of air conditioning and dehumidification equipment in the substation based on real-time monitored ambient temperature, humidity, and other data, optimizing the equipment operating environment, improving the stability and reliability of system operation, and realizing local intelligent management and control of coal mine power supply equipment.

[0029] The cloud platform decision-making hub integrates an equipment health management platform, a dynamic valley scheduling engine, a 3D digital twin module, a blockchain evidence storage module, an early warning unit, and an energy consumption verification module.

[0030] The health factor calculation unit of the equipment health management platform collects temperature, humidity, and dust concentration data in real time from multimodal sensing terminals. Taking a high-voltage switchgear in a coal mine substation as an example, temperature, humidity, and dust concentration data are transmitted to the health factor calculation unit via temperature sensors, humidity sensors, and dust concentration sensors installed inside the cabinet. This unit uses a specific algorithm to represent health as an exponential function multiplied by humidity offset and dust concentration, i.e., health = e^(k1*temperature)*(1+k2*humidity offset)*(1+k3*dust concentration), where k1, k2, and k3 are weighting coefficients determined through experiments and data analysis, used to measure the degree of influence of different factors on equipment health. Through this calculation model, the impact of temperature, humidity, and dust concentration on equipment health can be comprehensively considered, and the health status of the equipment can be assessed in real time, providing a quantitative basis for equipment maintenance and management.

[0031] The fault prediction unit utilizes a time-series prediction model, taking current harmonic distortion rate, peak vibration factor, and health time-series data as input features to predict the remaining lifespan of equipment. For example, for an operating transformer, it collects long-term operating data on current harmonic distortion rate (reflecting power quality and internal electromagnetic interference), peak vibration factor (reflecting the vibration intensity and impact characteristics of mechanical components), and health time-series data obtained through a health factor calculation unit. This data is input into a trained time-series prediction model. Based on learning and analysis of historical data, the model predicts the transformer's remaining lifespan over a future period, issuing early warnings for equipment maintenance. This allows for the rational scheduling of maintenance plans, reducing the risk of sudden equipment failures and improving the reliability and operational efficiency of the coal mine power supply system.

[0032] The dynamic valley scheduling engine includes an input interface and an optimization processor.

[0033] Input Interface: The input interface of the dynamic valley scheduling engine connects to external electricity market data sources, multimodal sensing terminals, and load forecasting systems to receive real-time electricity price signals, equipment health data, and load forecasting curves. For example, it obtains real-time electricity price fluctuation information by interfacing with the power grid company's electricity trading system; acquires health data of each device from the edge computing gateway; and uses the load forecasting model to predict the electricity load change curve for a future period based on coal mine production plans, historical electricity consumption data, and seasonal factors, providing comprehensive input information for energy consumption optimization scheduling.

[0034] Optimized Processor: The optimized processor is based on an energy consumption optimization model with safety constraints. It uses minimizing total electricity costs as the objective function, comprehensively considering equipment safety operation constraints, including real-time equipment temperature not exceeding a temperature threshold, equipment health not falling below a health threshold, and power fluctuation rate not exceeding a rated power threshold. Furthermore, considering the specific safety requirements of coal mines, when methane concentration reaches a safety threshold, power to non-explosion-proof equipment is immediately cut off; when carbon monoxide concentration reaches a safety threshold, ventilation equipment is activated and energy consumption optimization is suspended, prioritizing safe production in the coal mine. For example, during optimized scheduling, for a motor with temperature monitoring, its temperature is monitored in real time. If the temperature approaches a preset temperature threshold (e.g., 90℃), the motor's load distribution or running time is adjusted to lower the motor temperature, ensuring the equipment operates within a safe temperature range. Simultaneously, based on real-time equipment health assessment results, for equipment with low health (e.g., below 0.7), its operating load is reduced or maintenance is scheduled to prevent equipment failure due to poor health, which could affect the stable operation of the coal mine's power supply system. By optimizing processor scheduling, the operating time and power of various electrical equipment in the coal mine can be rationally arranged, making full use of off-peak electricity prices to reduce the coal mine's electricity costs while ensuring equipment safety and safe production in the coal mine.

[0035] The 3D digital twin module includes a virtual mapping unit and a risk visualization unit.

[0036] The virtual mapping unit utilizes 3D modeling technology to construct a detailed 3D model of the coal mine power supply system, including surface substations, underground substations, transmission lines, various electrical equipment, and their spatial relationships. Through real-time data connection with multimodal sensing terminals, electrical parameters (such as voltage, current, and power) and equipment status data (such as operating status, fault status, and health status) are mapped in real time to the corresponding equipment objects in the 3D model. For example, clicking on a transformer in the 3D model displays its real-time voltage, current, power factor, temperature, health status, and other parameters, as well as its connection relationships and power supply range within the coal mine power supply system. This provides managers with an intuitive and visual interface for the power supply system, facilitating real-time monitoring and analysis of the overall operation of the power supply system.

[0037] In a 3D digital twin model, the risk visualization unit marks the locations of high-risk equipment using color coding and icon labeling. Based on factors such as equipment health, operating status, and environment, the safety risks of equipment are assessed and classified. For example, equipment with a health level below 0.6 and operating in high-temperature, high-humidity environments is marked as high-risk equipment and its location is highlighted in red on the 3D model. Simultaneously, fault protection actions are simulated. When a potential equipment failure is predicted, the 3D model displays the action process and impact range of the protection devices after the failure, such as circuit breaker tripping and backup power activation, helping managers to develop contingency plans in advance. Furthermore, energy savings and safety risk distribution are displayed. By comparing energy consumption data before and after optimized scheduling, the 3D model visually presents the areas and values ​​of energy savings and displays the distribution of safety risks throughout the power supply system using heat maps and other formats. This provides decision support for energy and safety management, enabling intelligent and efficient control of the coal mine power supply system.

[0038] The early warning unit includes a real-time alarm unit, a fusion diagnostic unit, and a trend early warning unit.

[0039] The real-time alarm unit dynamically sets overload thresholds based on the temperature rise characteristics of the equipment. For different types of equipment, a temperature rise characteristic model is established based on their normal operating temperature change curves and overload capacity. For example, for an asynchronous motor, under rated load, its temperature gradually increases over time and tends to a stable value. When the load exceeds a certain limit, the rate of temperature rise accelerates. Based on this model, the motor's temperature and load current are monitored in real time, and the overload threshold is dynamically calculated. When the load current approaches the overload threshold, an alarm is immediately triggered, alerting management personnel to take timely measures to prevent equipment damage due to overload and ensure safe equipment operation.

[0040] The integrated diagnostic unit comprehensively considers multi-dimensional information such as current surges, vibration data, and gas concentrations to conduct a risk fusion assessment. For example, when the current surge in a certain area exceeds a set threshold (e.g., exceeding 30% of the normal current), and the vibration data of the equipment in that area shows abnormalities (e.g., increased vibration amplitude, changes in frequency composition) and a slight increase in methane or carbon monoxide concentration, the integrated diagnostic unit will perform fusion analysis on this information to calculate a comprehensive risk value. When the comprehensive risk value exceeds a preset threshold, it is determined that there may be a serious safety hazard in the area, triggering a shutdown command to cut off the power supply to the relevant equipment, preventing the occurrence and escalation of accidents, and ensuring the safety of coal mine personnel and equipment.

[0041] The trend early warning unit establishes a predictive model based on historical and real-time monitoring data to trigger equipment failure alarms in advance. Taking cable insulation aging as an example, historical data such as insulation resistance, temperature, and operating time during cable operation are collected to establish a predictive model for cable insulation aging, such as a grey prediction model or a neural network model. By monitoring parameters such as cable insulation resistance in real time and inputting the data into the predictive model, the trend of cable insulation aging is predicted, and early warnings of cable failure are issued. This provides sufficient time for cable maintenance and replacement, avoids power outages and safety accidents caused by cable insulation damage, and improves the reliability and safety of the coal mine power supply system.

[0042] The energy consumption verification module includes a strategy execution unit and a verification unit.

[0043] The strategy execution unit, based on energy consumption optimization scheduling instructions from the cloud platform's decision-making center, controls operations such as energy storage switching of charging equipment, start-stop regulation of drainage pumps, and reactive power compensation of ventilation fans. For example, during off-peak electricity price periods, it controls charging equipment to store energy in battery banks for use during peak electricity price periods, reducing electricity costs; based on mine water level changes and drainage needs, it rationally starts and stops drainage pumps, optimizing the drainage system's operating time and energy consumption; by monitoring the power factor of ventilation fans, it activates or deactivates reactive power compensation devices in real time to improve the power factor of ventilation fans, reduce line losses, and achieve energy-saving operation of the ventilation system. Through these strategy execution operations, the effective implementation of energy consumption optimization scheduling instructions is ensured, improving the energy utilization efficiency of the coal mine power supply system.

[0044] The verification unit calculates the deviation in electricity consumption per ton of coal based on actual energy consumption monitoring data and adjusts the scheduling parameters accordingly. For example, by statistically analyzing the coal production and total electricity consumption of a coal mine over a certain period, the actual value of electricity consumption per ton of coal is calculated and compared with the target value predicted by the energy consumption optimization model to obtain the deviation. Based on this deviation, problems in the energy consumption optimization scheduling process are analyzed, such as equipment operating efficiency not meeting expectations or unreasonable scheduling strategies. Scheduling parameters are then adjusted, such as adjusting equipment operating time and power allocation, to further optimize energy consumption management strategies, improve the accuracy and effectiveness of energy consumption management in the coal mine power supply system, and achieve the coal mine's energy conservation and consumption reduction goals.

[0045] The blockchain evidence storage module includes an instruction recording unit and an emergency response unit.

[0046] The instruction recording unit uses blockchain technology to store the hash value and timestamp of operation instructions. Whenever the cloud platform decision-making center or edge computing gateway issues an operation instruction, such as a device start / stop instruction or parameter adjustment instruction, the instruction recording unit first calculates the hash value of the instruction. A hash value is a fixed-length string generated after encrypting data, possessing uniqueness and immutability. Simultaneously, it records the timestamp of the instruction, accurate to the second, ensuring the temporal order and integrity of the operation instructions. The hash value and timestamp are stored in the distributed ledger of the blockchain, with each node maintaining a copy, ensuring data security and reliability and providing strong evidence for the traceability and auditing of operation instructions.

[0047] The emergency response unit monitors key safety parameters such as equipment health and methane concentration in real time. When equipment health or methane concentration reaches a dangerous threshold, the emergency response mechanism is immediately activated, freezing current dispatch instructions to prevent further equipment failure or safety accidents from being exacerbated by continuing to execute existing instructions. Simultaneously, a tamper-proof log containing hash values ​​of operation instructions, timestamps, and emergency response information is generated and stored on the blockchain. For example, if the health of a critical piece of equipment suddenly drops below a dangerous threshold (e.g., below 0.5), it may be due to a sudden equipment failure. In this case, the emergency response unit quickly freezes all dispatch instructions involving that equipment, such as power adjustment instructions and extended running time instructions, and generates a detailed emergency log recording the equipment status, dispatch instructions, and emergency response measures at the time of the incident. This provides accurate and reliable data support for accident investigation and follow-up, ensuring the safety and traceability of the coal mine power supply system in emergency situations.

[0048] The dual-channel redundant communication network specifically includes a data acquisition interface, a command transmission channel, and a priority controller.

[0049] The data acquisition interface is based on the industrial bus protocol. The interface sends requests periodically according to the protocol, receives and verifies parsed data to ensure accuracy. The command transmission channel is the link through which the cloud platform sends optimization commands to the edge gateway, built using Ethernet communication and TCP / IP protocols. An Ethernet switch is deployed between the ground control center cloud platform and the underground edge gateway to ensure link stability. To improve real-time performance, QoS policies are enabled on network devices, marking command transmission data packets as the highest priority and prioritizing their forwarding in case of network congestion. For example, when the cloud platform needs to adjust the drainage pump power, the command is quickly transmitted to the edge gateway via this channel for immediate control execution.

[0050] The priority controller ensures the highest transmission priority for safety interlock commands through both hardware and software. On the hardware side, dedicated circuits are designed to monitor and classify commands in real time, assigning the highest priority identifier to each safety interlock command. On the software side, algorithms are developed to dynamically adjust the transmission queue order. For example, when methane exceeds the limit and a power outage occurs, the generated power outage signal enters the network. The hardware circuit quickly identifies and separates the signal, and the software algorithm inserts it at the front of the queue, ensuring that the command is transmitted to the edge gateway immediately, triggering an immediate power outage to prevent accidents and safeguard underground safety.

[0051] 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 coal mine power supply management monitoring and early warning system, characterized in that: include: Multimodal sensing terminals are deployed in ground substations, underground substations, and key electrical equipment, including transient waveform voltage and current sensors, high-temperature resistant wireless vibration sensors, methane sensors, and infrared carbon monoxide and temperature dual-mode detectors. A dual-channel redundant communication network, comprising a backbone fiber optic ring network and an emergency channel based on long-distance wireless communication; Edge computing gateways are used for data preprocessing and rapid local control response; The cloud platform decision-making hub integrates an equipment health management platform, a dynamic valley scheduling engine, a 3D digital twin module, a blockchain evidence storage module, an early warning unit, and an energy consumption verification module.

2. The coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The device health management platform includes: The health factor calculation unit is used to calculate the health of the equipment based on temperature, humidity and dust concentration data. The health is the product of an exponential function and the humidity offset and dust concentration. The fault prediction unit is used to predict the remaining life of the equipment through a time-series prediction model. The input features include current harmonic distortion rate, vibration peak factor and health time-series data.

3. The coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The dynamic valley scheduling engine includes: The input interface is used to receive real-time electricity price signals, equipment health status, and load forecast curves. The optimized processor performs energy consumption optimization calculations with safety constraints, aiming to minimize total electricity costs. The constraints include: real-time equipment temperature not exceeding a temperature threshold; equipment health not falling below a health threshold; power fluctuation rate not exceeding a rated power threshold; cutting off power to non-explosion-proof equipment when methane concentration reaches a safety threshold; and activating ventilation equipment and pausing energy consumption optimization when carbon monoxide concentration reaches a safety threshold.

4. The coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The three-dimensional digital twin module includes: The virtual mapping unit is used to construct a three-dimensional model of the power supply system and map electrical parameters and equipment status in real time; The risk visualization unit is used to pinpoint the location of high-risk equipment, simulate fault protection actions, and display energy savings and safety risk distribution.

5. A coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The early warning unit includes: A real-time alarm unit is used to dynamically set the overload threshold based on the temperature rise characteristics; The fusion diagnostic unit is used to perform risk fusion on current surges, vibration data and gas concentration, and triggers a shutdown command when the comprehensive risk value exceeds the threshold. The trend warning unit is used to trigger equipment failure alarms in advance based on the prediction model.

6. The coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The energy consumption verification module includes: The strategy execution unit is used to control the energy storage switching of charging equipment, the start and stop regulation of drainage pumps, and the reactive power compensation of ventilation fans; The verification unit is used to calculate the deviation in electricity consumption per ton of coal and correct the scheduling parameters.

7. A coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The blockchain evidence storage module includes: The instruction recording unit is used to store the hash value and timestamp of the operation instruction; The emergency response unit freezes dispatch instructions and generates tamper-proof logs when the equipment health or methane concentration reaches a dangerous threshold.

8. A coal mine power supply management monitoring and early warning system according to claim 1, characterized in that, The dual-channel redundant communication network includes: The data acquisition interface adopts the industrial bus protocol; The command transmission channel is used by the cloud platform to send optimization commands to the edge gateway. The priority controller is used to set the safety interlock command as the highest transmission priority.

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