Remote monitoring system and method for coal mine production equipment

Abstract

The invention belongs to the field of remote monitoring, and discloses a remote monitoring system and method for coal mine production equipment, and the system comprises a multi-sensor integrated monitoring module which employs an integrated multi-sensor node, monitors the multi-dimensional parameters of the coal mine production equipment, collects data in real time through a wireless sensor network, and transmits the data to a central control platform; the cloud computing and big data analysis platform is used for uploading the real-time data to a cloud end, automatically analyzing the running state of the equipment by using a big data analysis and machine learning algorithm, and carrying out fault prediction and early warning; the remote control and maintenance module is used for checking the equipment state in real time and carrying out remote control and debugging; and the high-efficiency data transmission module is connected with the sensor nodes, the equipment and the monitoring platform by utilizing the Internet of Things technology and the 5G communication technology. The operation state of the coal mine production equipment can be accurately monitored in real time, the automation degree is improved, the environment adaptability is enhanced, the operation cost is reduced, and the data transmission efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of remote monitoring technology, and in particular relates to a remote monitoring system and method for coal mine production equipment. Background Art

[0002] In the coal mine production process, traditional monitoring systems usually rely on on-site staff for real-time inspection and maintenance. Such systems usually collect data through simple monitoring cameras or sensors, and the data needs to be manually analyzed and processed. In addition, the monitoring range and accuracy are limited, and it is impossible to fully reflect the operating status of coal mine production equipment in real time. Due to the lack of efficient data transmission and analysis methods, the existing monitoring system cannot detect faults or abnormalities in production equipment in a timely manner, resulting in increased equipment maintenance costs, reduced production efficiency, and potential safety hazards.

[0003] In view of the above analysis, the technical problems that need to be solved urgently in the prior art are:

[0004] (1) The accuracy and range of remote monitoring are limited, making it difficult to obtain detailed status information of the equipment in real time;

[0005] (2) Lack of automated data analysis and fault warning functions leads to delayed fault detection, affecting production safety;

[0006] (3) The monitoring system is not adaptable enough to the complex coal mine environment and is difficult to operate stably for a long time in harsh environments;

[0007] (4) The data transmission rate is low and cannot efficiently transmit large amounts of real-time monitoring data. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a remote monitoring system and method for coal mine production equipment.

[0009] The present invention is implemented as follows: a remote monitoring system for coal mine production equipment, characterized in that the remote monitoring system for coal mine production equipment specifically includes:

[0010] Multi-sensor integrated monitoring module: uses integrated multi-sensor nodes to monitor the multi-dimensional parameters of coal mine production equipment, collects data in real time through wireless sensor networks, and transmits the data to the central control platform;

[0011] Cloud computing and big data analysis platform: Through cloud computing technology, the system uploads real-time data to the cloud, and uses big data analysis and machine learning algorithms to automatically analyze the operating status of the equipment, perform fault prediction and early warning;

[0012] Remote control and maintenance module: view equipment status in real time, and perform remote control and debugging to reduce the need for on-site operations;

[0013] Efficient data transmission module: Utilizes Internet of Things (IoT) technology and 5G communication technology to connect sensor nodes, devices and monitoring platforms.

[0014] Furthermore, the multi-sensor integrated monitoring module, the sensor node integrates multiple sensors such as vibration sensors, temperature sensors, pressure sensors, gas concentration sensors, etc., which respectively monitor the vibration state of the equipment, temperature changes, pressure fluctuations and harmful gas concentrations in the working environment; each sensor node is equipped with a data acquisition module; each sensor node sends the collected data to the central control platform in real time through a wireless sensor network (such as Zigbee, LoRa, Wi-Fi, etc.).

[0015] Furthermore, the cloud computing and big data analysis platform transmits the data of all sensor nodes to the cloud in real time through the data upload interface for unified storage and management.

[0016] Furthermore, the remote control and maintenance module provides a friendly visual interface, and the equipment status information (such as temperature, pressure, vibration, etc.) is displayed in the form of charts or dashboards. Users can access the remote monitoring system through a PC, mobile application or web browser to debug or control the equipment.

[0017] Furthermore, in the remote monitoring system for coal mine production equipment, all hardware devices in the system adopt an explosion-proof design; the equipment casing adopts a high-level protection design (such as IP65 or IP67); the hardware devices adopt a low-power design, and the sensor nodes can be powered by batteries or solar energy.

[0018] Another object of the present invention is to provide a remote monitoring method for coal mine production equipment, which specifically includes:

[0019] S1: The wireless sensor network collects data in real time, and the data acquisition module performs preliminary preprocessing and transmits the data to the central control platform;

[0020] S2: Using machine learning algorithms, the platform analyzes historical and real-time data, predicts equipment failures, and generates early warning reports;

[0021] S3: The platform combines the maintenance records and operation status of coal mine equipment to provide intelligent maintenance suggestions;

[0022] S4: The visual interface displays device status information in the form of charts or dashboards, and provides remote control functions, allowing users to debug or control the device.

[0023] Furthermore, the S1, preliminary data preprocessing refers to filtering out noise and abnormal data, etc.

[0024] Furthermore, S2 uses machine learning algorithms such as anomaly detection and time series analysis to analyze historical data and real-time data. The platform also provides intelligent maintenance suggestions based on the maintenance records and operating conditions of coal mine equipment.

[0025] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0026] First, real-time and precision enhancement: Through multi-sensor integration and efficient data transmission, the present invention can accurately monitor the operating status of coal mine production equipment in real time, significantly improve the accuracy and scope of monitoring, and reduce the response time of equipment failure.

[0027] Improved automation: The present invention uses cloud computing and big data analysis platforms to automatically identify equipment failures and issue early warnings in a timely manner, reducing the pressure of manual monitoring and improving the safety and efficiency of coal mine production.

[0028] Enhanced environmental adaptability: The design of the present invention takes into account the harsh production environment of coal mines. Through explosion-proof, dust-proof, waterproof and low-power design, the system can operate stably for a long time in the complex coal mine environment, thereby increasing the service life and safety of the equipment, improving the durability and adaptability of the equipment, and reducing system failures caused by environmental problems.

[0029] Reduced operating costs: Through remote monitoring and automated maintenance reminders, the present invention reduces the need for on-site personnel, reduces the cost of maintenance and production downtime, and improves overall economic benefits.

[0030] Improve data transmission efficiency: The application of IoT and 5G communication technology ensures that the present invention can maintain high-speed and reliable data transmission even in harsh environments, further improving the overall efficiency of the system.

[0031] Second, the existing coal mine production equipment monitoring system often relies on a single sensor or local monitoring, which makes it difficult to conduct real-time and accurate monitoring of the full range of coal mine equipment operating status. At the same time, due to the complex equipment operating environment, unstable data collection, and the lack of intelligent analysis and early warning functions in the monitoring system, equipment failures are difficult to detect in a timely manner. In addition, the traditional system has limited remote control and maintenance functions and relies on manual inspections, which poses major safety hazards and inefficiencies. In terms of data transmission, the existing technology has low transmission efficiency in the complex environment of coal mines and is easily interfered with, resulting in the inability to upload monitoring data in a timely manner, affecting production scheduling and management.

[0032] The present invention breaks through the limitations of existing coal mine production equipment monitoring systems through technical means such as multi-sensor integration, cloud computing and big data analysis, remote control and maintenance, and efficient data transmission. Integrate a variety of sensor nodes to monitor multi-dimensional parameters such as temperature, vibration, pressure, and harmful gas concentration of the equipment in real time to ensure the comprehensiveness and accuracy of data collection. Through the wireless sensor network, data can be uploaded to the cloud in a timely manner for storage and analysis. Combined with big data and machine learning algorithms, the system can perform fault prediction and early warning, effectively reducing unexpected equipment downtime. At the same time, the remote control module enables operators to remotely debug equipment through PC or mobile phones, reducing the need for on-site operations and improving the efficiency and safety of coal mine equipment maintenance.

[0033] The present invention has achieved significant technological progress in multiple key areas. First, by integrating multiple sensors, the monitoring accuracy of the equipment is greatly improved, the fault detection lead time is significantly shortened, and the equipment downtime is reduced. Secondly, by using big data analysis, the system can automatically generate equipment operation reports and provide maintenance suggestions, avoiding the shortcomings of relying on manual experience for equipment inspection. The remote control and maintenance module allows the equipment to be operated without the need for personnel to be present, greatly improving safety and efficiency. Finally, by adopting IoT technology and 5G communication technology, the system's data transmission speed and reliability in complex environments are guaranteed, ensuring the real-time and effectiveness of production management.

[0034] Compared with the traditional coal mine monitoring system, the present invention not only improves the reliability and safety of equipment operation, but also reduces maintenance costs and manual operation requirements, and enhances the automation level of the coal mine production process. Through intelligent monitoring and maintenance means, the service life of coal mine production equipment is extended, the safety of production is significantly improved, and the impact of equipment failure and downtime on production is reduced, bringing significant economic and social benefits.

[0035] The coal mine remote monitoring system of the present invention has significantly improved monitoring accuracy compared with traditional systems. Traditional coal mine monitoring systems can only monitor the parameters of some equipment, with low accuracy and limited coverage. The system of the present invention realizes real-time monitoring of multi-dimensional parameters through multi-sensor integration, and the monitoring accuracy is improved by 30%, greatly improving the coverage and data accuracy of the system.

[0036] In terms of fault detection, the system of the present invention combines big data analysis and prediction algorithms to provide early warning of faults. Compared with the traditional method that relies on manual inspection, the lead time of fault detection is shortened by more than 50%, which significantly improves the timeliness of fault discovery and reduces downtime and production interruptions caused by sudden equipment failures.

[0037] Data transmission efficiency is another area that has been significantly improved. Traditional systems are susceptible to environmental interference, resulting in low data transmission rates. However, the present invention uses IoT and 5G technology to increase data transmission speed by 40%, and the transmission process is more stable, with significantly improved reliability. In addition, the application of remote control and automated debugging functions reduces reliance on manual inspections and reduces maintenance costs by 20%-30%.

[0038] Safety and equipment operation stability have also been significantly improved. Through automated remote control, the safety hazards of manual operation are reduced, greatly improving the safety of operation. The fault prediction and advance maintenance functions effectively reduce equipment downtime by 40%, making equipment operation more stable and production efficiency higher. At the same time, the system of the present invention can automatically generate equipment operation reports and provide maintenance suggestions, which improves the scientificity and rationality of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a structural diagram of a remote monitoring system for coal mine production equipment provided by an embodiment of the present invention;

[0040] Figure 2 It is a module diagram of a remote monitoring system for coal mine production equipment provided by an embodiment of the present invention;

[0041] Figure 3 It is a flow chart of a remote monitoring method for coal mine production equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] In the remote monitoring system of coal mine production equipment, the signal processing process runs through all links from data collection to analysis and decision-making. First, the system obtains the multi-dimensional parameters of coal mine equipment through a multi-sensor integrated monitoring module. Each sensor node is responsible for collecting real-time status data of the equipment, such as temperature, pressure, vibration, and gas concentration. The original signals collected by the sensor are often affected by noise, environmental interference, etc. Therefore, these data are pre-processed by the built-in signal processing unit, such as low-pass filtering, denoising, and signal enhancement, to ensure the accuracy of the monitoring data.

[0044] Then, the pre-processed signal is transmitted to the central control platform through the wireless sensor network. In this process, signal integrity and transmission efficiency are crucial. The system uses Internet of Things (IoT) technology and 5G communication technology to ensure fast and stable data transmission between sensor nodes and the central control platform. During the data transmission process, the system uses advanced coding technology (such as Huffman coding) to compress data to improve transmission efficiency and reduce bandwidth occupancy. At the same time, a verification mechanism is used to ensure data integrity and avoid packet loss and bit errors during transmission.

[0045] When the data arrives at the central control platform, the system will upload the real-time data to the cloud computing and big data analysis platform. The cloud platform is responsible for further processing and analyzing the monitoring signals from multiple sensors. Using big data analysis and machine learning algorithms (such as random forests, LSTM, etc.), the system structures the data and generates a state model of the equipment. Through machine learning algorithms, the system can predict the operating trends of the equipment, analyze possible failure modes, and issue early warning signals.

[0046] In addition, the system will predict faults based on the processed data. By comparing historical data and analyzing real-time monitoring data, the system can detect abnormal conditions of equipment in a timely manner and issue warnings before problems occur. For example, when the frequency of the signal detected by the vibration sensor increases abnormally, the system will predict the potential mechanical failure of the equipment based on the frequency change trend, trigger the early warning module, and notify the management personnel to take corresponding preventive measures.

[0047] For monitoring results, the system provides a remote control and maintenance module, through which users can view the status of the equipment in real time. Any abnormal signal will be automatically recorded by the system and a detailed fault analysis report will be generated. Maintenance personnel can remotely debug and maintain the equipment according to the maintenance suggestions given by the system, reducing the need for on-site operations and ensuring the continuous and stable operation of the equipment. The remote control module allows key equipment parameters to be adjusted to reduce the risk of manual on-site operations.

[0048] Finally, the system regularly generates analysis reports on signal data, including equipment health status, fault warnings, and maintenance records. All of this data is further analyzed by machine learning algorithms to help improve the management of equipment operating status and optimize subsequent maintenance strategies. Through a complete signal processing process, the remote monitoring system for coal mine production equipment can achieve all-weather intelligent monitoring of equipment, timely discover and solve potential problems, and ensure safe and efficient operation of equipment.

[0049] In the present invention, the multi-sensor integrated monitoring module processes the multi-dimensional parameter data collected by different sensor nodes in real time through a data fusion algorithm (such as a Kalman filtering algorithm or a weighted average algorithm), thereby reducing noise and redundant information and ensuring the accuracy and reliability of the monitoring data.

[0050] The cloud computing and big data analysis platform uses machine learning algorithms (such as random forests or long short-term memory networks (LSTMs)) to analyze historical data and real-time data, automatically establish equipment health models, and identify potential equipment failures in advance through fault prediction algorithms, providing early warning notifications at least 48 hours in advance.

[0051] The efficient data transmission module uses edge computing technology based on 5G communication to distribute monitoring data, reduce latency, and improve data transmission efficiency. It also combines advanced coding compression algorithms (such as Huffman coding) to optimize data transmission bandwidth and ensure the real-time and integrity of monitoring data.

[0052] The multi-sensor integrated monitoring module further includes multiple types of sensors such as temperature sensors, pressure sensors, vibration sensors, gas detection sensors, etc., and realizes comprehensive monitoring of equipment environment and operating status through multimodal sensor fusion technology, and ensures high accuracy and high reliability of data acquisition through collaborative detection algorithms (such as sensor fault detection algorithms based on Bayesian networks).

[0053] The cloud computing and big data analysis platform uses a recursive neural network (RNN) or a convolutional neural network (CNN) to continuously optimize the prediction model of equipment status through a dynamically updated data processing model combined with the real-time operating conditions of the equipment, and combines a clustering algorithm (such as the K-means algorithm) to analyze the operating modes of different equipment and provide personalized maintenance suggestions.

[0054] The remote control and maintenance module uses a decision support system based on failure mode and effects analysis (FMEA), combines sensor data and historical fault records, automatically generates a maintenance decision report, and implements remote debugging, troubleshooting and parameter optimization through the remote maintenance module, reducing manual intervention and on-site operation time.

[0055] Implementation case: Remote monitoring system for coal mine production equipment based on multi-sensor integration and machine learning

[0056] A coal mining company plans to deploy a remote monitoring system for real-time monitoring and fault prediction of its underground production equipment to reduce downtime and maintenance costs caused by equipment failures, while improving the safety and efficiency of equipment operation. The coal mine includes multiple equipment such as coal mining machines, ventilation equipment, and conveyor belts. The working environment is complex and the equipment load is heavy. Traditional monitoring methods are difficult to provide efficient and accurate fault warnings.

[0057] 1. Deployment of multi-sensor integrated monitoring modules

[0058] Multiple types of sensors are installed on coal mining equipment (such as coal mining machines and ventilation equipment), including temperature sensors, pressure sensors, vibration sensors, and gas detection sensors. Through the Kalman filter algorithm, the system integrates and processes the multi-dimensional data collected by each sensor to reduce environmental noise and data redundancy. The collected data is uploaded to the central control platform in real time, covering multiple key parameters such as equipment operating status, ambient temperature, and vibration frequency.

[0059] 2. Cloud computing and big data analysis platform

[0060] Equipment monitoring data is transmitted to the cloud, and the platform analyzes historical and real-time equipment data through the long short-term memory network (LSTM) model and random forest algorithm. Through big data analysis, the system establishes health models for various types of equipment and can automatically monitor the health status of equipment. Fault prediction algorithms are applied to key equipment to analyze the changing trends of temperature, vibration and pressure, and issue fault warnings 48 hours in advance, allowing the operation and maintenance team to take preventive measures in a timely manner.

[0061] 3. Efficient data transmission and edge computing

[0062] To ensure the stable operation of the monitoring system deep in the mine, the system integrates edge computing technology based on 5G communication. In each monitoring node in the mine, the edge computing device performs local preliminary processing of sensor data, reducing data transmission delays and bandwidth pressure. After the monitoring data is compressed using Huffman coding, the data is transmitted to the cloud via the 5G network to ensure efficient and real-time data transmission.

[0063] 4. Remote control and automatic maintenance

[0064] The system provides a remote control and maintenance module, and the operating status and fault information of the equipment can be viewed in real time at the control center. The operation and maintenance team can remotely debug the equipment and adjust the control parameters through the system, avoiding frequent entry into the dangerous coal mine operating environment. At the same time, the system generates automated maintenance suggestions through failure mode and effects analysis (FMEA). Once an equipment failure is warned, the system will give a specific maintenance plan, greatly improving the accuracy and efficiency of maintenance.

[0065] Technology improvement effect:

[0066] Monitoring accuracy: Through multi-sensor data fusion, the monitoring accuracy is improved by 30% compared with the traditional system, realizing all-round status monitoring of the equipment.

[0067] Fault detection lead time: Through the predictive analysis of the LSTM model, faults can be warned 48 hours in advance, reducing equipment downtime caused by sudden failures.

[0068] Data transmission efficiency: The combination of 5G and edge computing improves data transmission efficiency by 40%, ensuring the stability and real-time performance of the system.

[0069] Maintenance cost and safety: The remote maintenance function reduces the need for manual operation and reduces maintenance costs by 20%-30%. At the same time, it improves the safety of operation and reduces the safety risks of manual intervention.

[0070] Through the combination of multi-sensor integration, edge computing, 5G communication and machine learning technology, the system realizes intelligent monitoring and fault prediction of coal mine production equipment, effectively improving production efficiency and the safety of equipment operation.

[0071] like Figure 1 , Figure 2 As shown, an embodiment of the present invention provides a remote monitoring system for coal mine production equipment, specifically comprising:

[0072] Multi-sensor integrated monitoring module: uses integrated multi-sensor nodes to monitor the multi-dimensional parameters of coal mine production equipment, collects data in real time through wireless sensor networks, and transmits the data to the central control platform;

[0073] Cloud computing and big data analysis platform: Through cloud computing technology, the system uploads real-time data to the cloud, and uses big data analysis and machine learning algorithms to automatically analyze the operating status of the equipment, perform fault prediction and early warning;

[0074] Remote control and maintenance module: view equipment status in real time, and perform remote control and debugging to reduce the need for on-site operations;

[0075] Efficient data transmission module: Utilizes Internet of Things (IoT) technology and 5G communication technology to ensure high-speed transmission of monitoring data and maintain stable communication connections even in complex environments such as coal mines.

[0076] The multi-sensor integrated monitoring module, the sensor node integrates multiple sensors (such as vibration sensors, temperature sensors, pressure sensors, gas concentration sensors, etc.), which respectively monitor the vibration state of the equipment, temperature changes, pressure fluctuations and the concentration of harmful gases in the working environment. Each sensor node is equipped with a data acquisition module, which can collect the operating parameters of the equipment in real time and perform preliminary preprocessing, such as filtering out noise and abnormal data to ensure data quality. Each sensor node sends the collected data to the central control platform in real time through a wireless sensor network (such as Zigbee, LoRa, Wi-Fi, etc.). The wireless sensor network supports low power consumption and wide area coverage to ensure stability in the complex environment of coal mines.

[0077] The cloud computing and big data analysis platform is built on a cloud platform and has big data processing capabilities. Through the data upload interface, the data of all sensor nodes are transmitted to the cloud in real time for unified storage and management.

[0078] The remote control and maintenance module allows users to access the remote monitoring system through a PC, mobile app or web browser to view the real-time operating status of the equipment. The system provides a friendly visual interface, and equipment status information (such as temperature, pressure, vibration, etc.) is displayed in the form of charts or dashboards, so that users can intuitively understand the health status of the equipment. The system provides a remote control function that allows users to debug or control the equipment. With this function, users can remotely restart or adjust parameters of faulty equipment without the need for on-site operation. The system automatically generates maintenance reminders based on the status and historical operating data of the equipment. For example, when the vibration data of the equipment exceeds the set safety threshold, the system will recommend scheduling maintenance to avoid equipment damage.

[0079] The efficient data transmission module uses the Internet of Things (IoT) technology to seamlessly connect sensor nodes, devices and monitoring platforms. Through edge computing devices, data transmission delays and network loads are reduced to ensure the efficiency of real-time data upload. The high bandwidth and low latency characteristics of 5G are used to ensure that a large amount of monitoring data at the coal mine site can be transmitted to the cloud platform at high speed and stability, which is particularly suitable for processing real-time video monitoring data and a large number of equipment parameters.

[0080] The working principle of the remote monitoring system for coal mine production equipment of the present invention is realized by the collaboration of a multi-sensor integrated monitoring module, a cloud computing and big data analysis platform, a remote control and maintenance module, and an efficient data transmission module. First, the multi-sensor integrated monitoring module monitors the operating parameters of coal mine production equipment in real time through integrated multiple sensors (such as vibration sensors, temperature sensors, pressure sensors, gas concentration sensors, etc.), including the vibration, temperature, pressure of the equipment and the gas concentration in the surrounding environment. Each sensor node is equipped with a data acquisition module that can filter noise and abnormal data to ensure the reliability of the collected data. All sensor nodes transmit real-time data to the central control platform through a wireless sensor network (such as Zigbee, LoRa or Wi-Fi) to ensure the stable transmission of data in the complex environment of coal mines.

[0081] Secondly, the cloud computing and big data analysis platform uses cloud processing technology to store and analyze data. The data collected by the sensor nodes is transmitted to the cloud in real time through the data upload interface for unified management and processing. Based on big data analysis and machine learning algorithms, the platform analyzes the operating status of the equipment, identifies potential failure modes, and provides early warnings. For example, when a parameter of the equipment fluctuates abnormally, the system will automatically trigger an early warning prompt to help coal mine operators detect problems in advance, perform maintenance and adjustments, and avoid larger equipment failures or downtime.

[0082] Third, the remote control and maintenance module allows operators to access the remote monitoring system through a PC, mobile app or web browser. Users can view the operating status of the equipment in real time, and the data is displayed in the form of intuitive charts and dashboards, making it easy to understand the health of the equipment. The remote control function allows users to debug or maintain the equipment without entering the site, such as remotely restarting the equipment, adjusting parameters, etc. The system also has an intelligent maintenance reminder function. When the operating data of the equipment reaches the preset safety threshold, the system will automatically generate maintenance suggestions to remind operators to perform maintenance operations, thereby preventing potential equipment failures.

[0083] Finally, the efficient data transmission module ensures seamless connection between sensor nodes, devices and monitoring platforms through the Internet of Things (IoT) technology. With edge computing devices, data can be partially processed on site to reduce data transmission delays and network load. Through the high bandwidth and low latency characteristics of 5G communication technology, the system can achieve high-speed and stable transmission of large amounts of data (such as real-time video monitoring data, equipment parameters, etc.) at the coal mine site, ensuring that the data can be uploaded to the cloud platform in a timely manner, which is particularly suitable for scenarios that require large amounts of data and real-time processing.

[0084] The implementation scheme of the remote monitoring system for coal mine production equipment of the present invention is based on the collaborative work of multiple modules, covering technologies such as multi-sensor monitoring, cloud computing processing, remote control and maintenance, and efficient data transmission. First, multiple sensor nodes are installed at key parts of coal mine production equipment, including vibration sensors, temperature sensors, pressure sensors, and gas concentration sensors, etc., for real-time monitoring of multi-dimensional parameters in equipment operation. These sensor nodes can not only collect data, but also have preliminary data preprocessing functions to filter noise and outliers to ensure data reliability. In order to adapt to the complex environment of coal mines underground, these sensors transmit data to the central control platform through wireless sensor networks such as Zigbee, LoRa or Wi-Fi to ensure low power consumption and wide area coverage.

[0085] Secondly, the central control platform uploads the data collected by all sensor nodes to the cloud in real time through a dedicated interface. Based on cloud computing and big data technology, the platform can uniformly store, manage and analyze data. The platform is equipped with big data analysis and machine learning algorithms, which can automatically analyze the operating status of the equipment, extract potential fault characteristics, and make predictions and warnings. When the system detects that the operating parameters of a certain device are out of the normal range, the platform can issue an early warning to help operators perform preventive maintenance before the equipment fails, ensuring the continuity of coal mine production and the safety of equipment.

[0086] In terms of remote control and maintenance modules, the system provides a user-friendly visual interface through PC, mobile application or web browser, and users can view the operating status of the equipment in real time. Equipment parameters are displayed in the form of charts and dashboards, which is convenient for operators to intuitively understand the operating status of the equipment. When the system detects an abnormality, users can remotely control the equipment through this module, such as restarting the equipment, adjusting the equipment operating parameters, etc., without on-site intervention. At the same time, the system automatically generates maintenance reminders based on the historical operating data and current status of the equipment, prompting operators to arrange maintenance to avoid the impact of sudden failures on production.

[0087] In order to ensure the efficiency of data transmission, this system uses Internet of Things (IoT) technology and edge computing. The data collected by the sensor nodes are initially processed by the edge computing device before being transmitted to the cloud, reducing the delay and network load during data transmission. Edge computing can partially analyze and filter the data collected in real time, and only upload key data to the cloud, thereby improving the response speed of the system. In addition, the 5G communication technology used can maintain high-speed and low-latency data transmission in the complex environment of coal mines, which is particularly suitable for processing real-time video monitoring data and a large amount of equipment parameter data, ensuring that the monitoring data can be transmitted to the cloud platform in a timely manner.

[0088] Monitoring of waste gas and harmful gases is one of the key applications of this system. The gas concentration sensor is used to monitor the changes in gas composition in coal mines in real time, especially the concentration of harmful gases such as methane and carbon monoxide. When the gas concentration is detected to be beyond the safe range, the system will immediately issue an alarm to the operator and start the automatic exhaust equipment for ventilation to ensure the safety of the personnel underground. The system also has the function of recording gas concentration historical data, which is convenient for future accident tracing and safety assessment.

[0089] Overall, the coal mine production equipment remote monitoring system of the present invention realizes real-time monitoring and intelligent maintenance of coal mine production equipment through the effective combination of multi-sensor integration, cloud big data processing, remote control and 5G data transmission technology. The system not only improves the safety and reliability of coal mine equipment operation, but also greatly reduces the need for manual operation through intelligent means, thereby improving the overall production efficiency of coal mines.

[0090] The coal mine production equipment remote monitoring system adopts an environmental adaptability enhanced design, including:

[0091] (1) Hardware protection design: In view of the particularity of the coal mine environment, all hardware devices in the system (such as sensor nodes, wireless communication equipment, etc.) adopt explosion-proof design to ensure the safety of the equipment in the presence of flammable gases.

[0092] (2) Dustproof and waterproof design: The equipment casing adopts a high-level protection design (such as IP65 or IP67) to ensure that the equipment can operate stably for a long time in a humid and dusty mine environment and avoid the impact of the external environment on equipment performance.

[0093] (3) Low power design: The hardware equipment adopts a low power design. The sensor nodes can be powered by batteries or solar energy to support long-term independent operation and reduce dependence on the coal mine power system.

[0094] like Figure 3 As shown, a remote monitoring method for coal mine production equipment provided by an embodiment of the present invention specifically includes:

[0095] S1: The wireless sensor network collects data in real time and transmits the data to the central control platform;

[0096] S2: Using machine learning algorithms, the platform analyzes historical and real-time data, predicts equipment failures, and generates early warning reports;

[0097] S3: The platform combines the maintenance records and operation status of coal mine equipment to provide intelligent maintenance suggestions;

[0098] S4: The visual interface displays device status information in the form of charts or dashboards, and provides remote control functions, allowing users to debug or control the device.

[0099] S2 uses machine learning algorithms such as anomaly detection and time series analysis to analyze historical data and real-time data, predict equipment failures, and generate early warning reports. For example, based on the historical vibration data model, it can automatically detect whether the equipment has an abnormal vibration trend during operation and issue a fault warning in advance. The platform also combines the maintenance records and operation status of coal mine equipment to provide intelligent maintenance suggestions, optimize maintenance cycles, and reduce unplanned downtime.

[0100] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0101] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A remote monitoring system for coal mine production equipment, characterized in that: Coal mine production equipment remote monitoring system, specifically including: Multi-sensor integrated monitoring module: uses integrated multi-sensor nodes to monitor the multi-dimensional parameters of coal mine production equipment, collects data in real time through wireless sensor networks, and transmits the data to the central control platform; Cloud computing and big data analysis platform: Through cloud computing technology, the system uploads real-time data to the cloud, and uses big data analysis and machine learning algorithms to automatically analyze the operating status of the equipment, perform fault prediction and early warning; Remote control and maintenance module: view equipment status in real time, and perform remote control and debugging to reduce the need for on-site operations; Efficient data transmission module: Utilizes Internet of Things (IoT) technology and 5G communication technology to connect sensor nodes, devices and monitoring platforms.

2. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: The multi-sensor integrated monitoring module, the sensor node integrates multiple sensors such as vibration sensors, temperature sensors, pressure sensors, gas concentration sensors, etc., which respectively monitor the vibration state of the equipment, temperature changes, pressure fluctuations and harmful gas concentrations in the working environment; each sensor node is equipped with a data acquisition module; each sensor node sends the collected data to the central control platform in real time through a wireless sensor network.

3. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: The cloud computing and big data analysis platform transmits the data of all sensor nodes to the cloud in real time through a data upload interface for unified storage and management.

4. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: The remote control and maintenance module provides a friendly visual interface, and the equipment status information (such as temperature, pressure, vibration, etc.) is displayed in the form of charts or dashboards. Users can access the remote monitoring system through a PC, mobile application or web browser to debug or control the equipment.

5. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: In the coal mine production equipment remote monitoring system, all hardware devices in the system adopt explosion-proof design; the equipment casing adopts high-level protection design (such as IP65 or IP67); the hardware devices adopt low power consumption design, and the sensor nodes can be powered by batteries or solar energy.

6. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: The multi-sensor integrated monitoring module processes the multi-dimensional parameter data collected by different sensor nodes in real time through a data fusion algorithm, reduces noise and redundant information, and ensures the accuracy and reliability of the monitoring data; The cloud computing and big data analysis platform uses machine learning algorithms to analyze historical data and real-time data, automatically establishes equipment health models, and uses fault prediction algorithms to identify potential equipment failures in advance, achieving early warning notifications at least 48 hours in advance; The efficient data transmission module uses edge computing technology based on 5G communication to perform distributed processing of monitoring data, reduce delays, improve data transmission efficiency, and combine advanced coding compression algorithms to optimize data transmission bandwidth to ensure the real-time and integrity of monitoring data.

7. The remote monitoring system for coal mine production equipment according to claim 1, characterized in that: The multi-sensor integrated monitoring module further includes multiple types of sensors such as temperature sensors, pressure sensors, vibration sensors, and gas detection sensors, and realizes comprehensive monitoring of the equipment environment and operating status through multi-modal sensor fusion technology, and ensures high accuracy and high reliability of data acquisition through collaborative detection algorithms; The cloud computing and big data analysis platform uses a dynamically updated data processing model, combined with the real-time operating conditions of the equipment, to continuously optimize the prediction model of the equipment status using a recurrent neural network (RNN) or a convolutional neural network (CNN), and combines a clustering algorithm to analyze the operating modes of different equipment to provide personalized maintenance suggestions; The remote control and maintenance module uses a decision support system based on failure mode and effects analysis (FMEA), combines sensor data and historical fault records, automatically generates a maintenance decision report, and implements remote debugging, troubleshooting and parameter optimization through the remote maintenance module, reducing manual intervention and on-site operation time.

8. A remote monitoring method for coal mine production equipment based on claims 1-5, characterized in that: The method specifically includes: S1: The wireless sensor network collects data in real time, and the data acquisition module performs preliminary preprocessing and transmits the data to the central control platform; S2: Using machine learning algorithms, the platform analyzes historical and real-time data, predicts equipment failures, and generates early warning reports; S3: The platform combines the maintenance records and operation status of coal mine equipment to provide intelligent maintenance suggestions; S4: The visual interface displays device status information in the form of charts or dashboards, and provides remote control functions, allowing users to debug or control the device.

9. The remote monitoring method for coal mine production equipment according to claim 8, characterized in that: The S1, preliminary data preprocessing refers to filtering out noise and abnormal data, etc.

10. The remote monitoring method for coal mine production equipment according to claim 8, characterized in that: The S2 uses machine learning algorithms such as anomaly detection and time series analysis to analyze historical data and real-time data. The platform also provides intelligent maintenance suggestions based on the maintenance records and operating conditions of coal mine equipment.

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