Coal mine electromechanical transportation intelligent monitoring system based on Internet of Things

Abstract

The invention discloses a coal mine electromechanical transportation intelligent monitoring system based on the Internet of Things, which comprises a data acquisition and preprocessing module based on a system architecture, a real-time monitoring and early warning module and a remote operation and maintenance and management module, and is characterized in that the data acquisition and preprocessing module adopts a layered architecture design; and the real-time monitoring and early warning module adopts an event-driven design mode. Compared with the prior art, the system has the advantages that (1) the data acquisition and preprocessing module adopts a layered architecture design, so that the data acquisition and processing efficiency is improved, and a high-quality data basis is provided for subsequent analysis; (2) the real-time monitoring and early warning module adopts a highly customizable graphical user interface, so that the user experience is improved, and real-time monitoring and scheduling are facilitated for managers; and (3) the remote operation and maintenance and management module adopts a micro-service architecture design, so that intelligent fault diagnosis and operation and maintenance management functions are realized, and the operation and maintenance efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine electromechanical transportation monitoring, and in particular to an intelligent monitoring system for coal mine electromechanical transportation based on the Internet of Things. Background Art

[0002] In the coal mine production process, the operating status of electromechanical transportation equipment is directly related to production efficiency and safety. Traditionally, the monitoring of electromechanical transportation equipment in coal mines mainly relies on manual inspections and simple sensor monitoring, which has many shortcomings. For example, manual inspections are not only inefficient, but also difficult to detect equipment failures in real time; and although simple sensor monitoring can provide some basic operating data, it lacks intelligent analysis and early warning functions, making it difficult to identify potential failures in a timely and accurate manner.

[0003] With the rapid development of Internet of Things technology, its application in the field of electromechanical transportation monitoring in coal mines is becoming more and more extensive. However, there are still some shortcomings in the relevant Internet of Things monitoring systems on the market. For example, a coal mine transportation monitoring method and system based on the Internet of Things disclosed in Patent No. CN202410633875.9, although it realizes the basic monitoring and early warning functions of the equipment, still has many limitations in data collection and processing, real-time monitoring and early warning, and remote operation and management. Specifically, the data collection and processing module lacks an efficient layered architecture design, resulting in insufficient data preprocessing capabilities; the real-time monitoring and early warning module lacks a highly customizable graphical user interface, and the user experience is poor; the remote operation and management module has a single function and lacks intelligent fault diagnosis and operation and maintenance management functions. Summary of the invention

[0004] The technical problems to be solved by the present invention are: (1) the data acquisition and processing module lacks an efficient layered architecture design, resulting in insufficient data preprocessing capabilities; (2) the real-time monitoring and early warning module lacks a highly customizable graphical user interface, resulting in a poor user experience; (3) the remote operation and management module has a single function and lacks intelligent fault diagnosis and operation and maintenance management functions.

[0005] In order to solve the above technical problems, the technical solution provided by the present invention is: an intelligent monitoring system for electromechanical transportation of coal mines based on the Internet of Things, including a data acquisition and preprocessing module based on the system architecture, a real-time monitoring and early warning module, and a remote operation and management module. The data acquisition and preprocessing module adopts a hierarchical architecture design, which includes a data acquisition layer, a data transmission layer and a data preprocessing layer. The data acquisition layer is responsible for obtaining raw data from the sensor network; the data transmission layer ensures the safe and efficient transmission of data; the data preprocessing layer cleans, converts the format and performs preliminary analysis on the data; the data acquisition and preprocessing module is internally divided into multiple independent but mutually cooperative components, including a sensor interface component, a data cache component, a data cleaning component, etc., to facilitate the expansion and maintenance of the system; the functions of the data acquisition and preprocessing module include: (1) real-time data acquisition: real-time acquisition of various physical parameters of electromechanical transportation equipment in coal mines through a sensor network; (2) data transmission and encryption: ensuring that the collected data can be safely and quickly transmitted to the system center, and using appropriate encryption technology to protect data security; (3) data preprocessing: cleaning the raw data, removing outliers and noise, and performing format conversion to provide a high-quality data basis for subsequent analysis;

[0006] The real-time monitoring and early warning module adopts an event-driven design mode. When receiving data or trigger conditions from the data acquisition and preprocessing module, it automatically performs corresponding monitoring and early warning operations. It includes a highly customizable graphical user interface for real-time display of equipment operation status and early warning information. The functions of the real-time monitoring and early warning module include: (1) Real-time monitoring: Display the real-time operation status of the equipment in a graphical manner, including position, speed, and load information; (2) Historical data playback: Supports query and playback of historical data, which facilitates management personnel to conduct post-analysis and decision-making; (3) Fault early warning: Use machine learning algorithms to monitor and predict the equipment operation status in real time. Once an abnormality or potential fault trend is found, an early warning signal is immediately triggered;

[0007] The remote operation and management module adopts a microservice architecture design, dividing the module into fault diagnosis services, operation and maintenance management services, etc., to facilitate system expansion and upgrading; it contains a data warehouse for storing and analyzing historical data and operation and maintenance records of equipment, and uses big data analysis technology to predict faults and optimize operation and maintenance; the functions of the remote operation and management module include: (1) Remote fault diagnosis: using big data analysis technology and expert systems to conduct in-depth analysis of equipment data, identify fault types and causes, and provide remote fault diagnosis reports and recommended maintenance plans; (2) Operation and maintenance management: integrating equipment maintenance records, maintenance plans, and spare parts inventory information to realize the information management of equipment maintenance work, including maintenance task allocation, progress tracking, and spare parts procurement; (3) Report generation and sharing: automatically generating operation and maintenance reports and fault diagnosis reports, supporting export and sharing functions, to facilitate management personnel to make decisions and communicate.

[0008] Furthermore, the data acquisition and preprocessing module functionally includes a sensor network module and a data preprocessing module. The sensor network module is deployed at various key positions of the electromechanical transportation equipment in coal mines to collect a variety of physical parameters including equipment vibration, temperature, pressure, and speed in real time, and transmits these collected data to the system center through the Internet of Things communication protocol; the data preprocessing module receives data from the sensor network module, performs data cleaning, format conversion and preliminary analysis, eliminates abnormal data and noise, and provides an accurate data basis for subsequent data analysis and fault diagnosis.

[0009] Furthermore, the real-time monitoring and early warning module functionally includes a real-time monitoring display module and a fault early warning module. The real-time monitoring display module displays the real-time operating status of coal mine electromechanical transportation equipment in a graphical interface, including the location, speed, and load information of each equipment, and supports historical data playback, which is convenient for management personnel to monitor and dispatch in real time; the fault early warning module uses machine learning algorithms to monitor and predict the equipment operating status in real time based on the data provided by the data preprocessing module. Once an abnormality or potential fault trend is found, a warning signal is triggered immediately, and relevant personnel are informed through SMS, email or system notification.

[0010] Furthermore, the remote operation and maintenance and management module functionally includes a remote fault diagnosis module and an operation and maintenance management module. The remote fault diagnosis module uses big data analysis technology and expert systems to conduct in-depth analysis of the collected equipment data, identify the type and cause of equipment failures, and provide remote fault diagnosis reports and recommended maintenance plans; the operation and maintenance management module integrates equipment maintenance records, maintenance plans, and spare parts inventory information to achieve information management of equipment maintenance work, including maintenance task allocation, progress tracking, and spare parts procurement, thereby improving operation and maintenance efficiency and resource utilization.

[0011] Furthermore, the data acquisition and preprocessing module transmits data with the sensor network through the standard Internet of Things communication protocol, and passes the preprocessed data to the real-time monitoring and early warning module through the internal interface or message queue for further analysis and display.

[0012] Furthermore, the real-time monitoring and early warning module receives the pre-processed data, performs real-time monitoring and early warning analysis, and passes the early warning information to the remote operation and management module through the internal interface or message queue for remote fault diagnosis and operation and maintenance management. It interacts with the user through the GUI, displays real-time monitoring data and early warning information, and receives user instructions and input.

[0013] Furthermore, the remote operation and management module receives warning information, performs remote fault diagnosis and operation and maintenance management, indirectly obtains equipment data through the real-time monitoring and warning module for fault analysis and operation and maintenance optimization, interacts with users through a web interface or mobile application, displays operation and maintenance reports, fault diagnosis results and operation and maintenance management information, and receives user instructions and input.

[0014] Compared with the prior art, the advantages of the present invention are as follows: (1) the data acquisition and preprocessing module adopts a layered architecture design, which improves the efficiency of data acquisition and processing and provides a high-quality data basis for subsequent analysis; (2) the real-time monitoring and early warning module adopts a highly customizable graphical user interface, which improves the user experience and facilitates real-time monitoring and scheduling by management personnel; (3) the remote operation and management module adopts a microservice architecture design, which realizes intelligent fault diagnosis and operation and maintenance management functions, and improves operation and maintenance efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the architecture of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0017] Embodiment 1

[0018] like Figure 1As shown, this embodiment provides an intelligent monitoring system for electromechanical transportation of coal mines based on the Internet of Things, including a data acquisition and preprocessing module, a real-time monitoring and early warning module, and a remote operation and management module based on the system architecture. Among them, the data acquisition and preprocessing module adopts a layered architecture design, including a data acquisition layer, a data transmission layer, and a data preprocessing layer to achieve efficient data acquisition, transmission, and processing; the real-time monitoring and early warning module adopts an event-driven design mode, including a highly customizable graphical user interface, to achieve real-time monitoring, historical data playback, and fault early warning functions; the remote operation and management module adopts a microservice architecture design, including a remote fault diagnosis module and an operation and maintenance management module, to achieve intelligent fault diagnosis and operation and maintenance management functions.

[0019] The system includes several key components: (1) Perception layer: including various sensors (such as temperature sensors, pressure sensors, vibration sensors, position sensors, etc.) installed on the electromechanical transportation equipment of coal mines, which are used to collect the working status and environmental parameters of the equipment in real time. (2) Network layer: using wireless communication technology (such as Wi-Fi, LoRa, Zigbee, etc.) to transmit the data collected by the perception layer to the data processing center, while supporting remote access and control. (3) Data processing and analysis layer: receiving data from the perception layer, preprocessing, storing and analyzing, using machine learning algorithms to identify abnormal patterns, predict equipment failures, and generate intelligent scheduling strategies. (4) Application layer: providing a user-friendly interface, displaying real-time monitoring data, early warning information, scheduling instructions, etc., supporting remote monitoring and decision support. (5) Database and storage: storing historical data, analysis results, configuration information, etc., supporting data query, analysis and report generation.

[0020] Embodiment 2

[0021] Based on the first embodiment, Figure 1 As shown, the workflow of this system is:

[0022] (1) Data collection: Sensors collect operating parameters and environmental data of coal mine electromechanical transportation equipment in real time. The data is transmitted to the data processing center through the Internet of Things network.

[0023] (2) Data preprocessing: Clean, denoise and convert the received data to ensure data quality. Store the preprocessed data in a database for subsequent analysis.

[0024] (3) Real-time monitoring and early warning: The system analyzes the equipment operating status in real time and identifies abnormal patterns. When a potential fault or abnormality is detected, an early warning signal is immediately triggered and the relevant personnel are informed via SMS, email or system notification.

[0025] (4) Intelligent scheduling and optimization: Based on real-time monitoring data and historical data, machine learning algorithms are used to predict equipment failure trends. Based on the prediction results, intelligent scheduling strategies are generated to optimize the transportation process and avoid equipment overload or idleness.

[0026] (5) Reporting and decision support: Generate detailed monitoring reports, including equipment operating status, fault warnings, scheduling strategies, etc. Provide decision support functions to help managers develop more reasonable production plans and maintenance strategies.

[0027] Working principle of this system: (1) Internet of Things technology: Through sensor networks and wireless communication technology, real-time collection and remote transmission of equipment data are realized. (2) Data analysis and machine learning: Using data analysis algorithms and machine learning models, deep mining and intelligent analysis of equipment data are carried out to identify abnormal patterns and predict equipment failures. (3) Intelligent decision-making and scheduling: Based on the analysis results, intelligent scheduling strategies are automatically generated to optimize transportation processes and improve production efficiency. (4) Human-computer interaction: Provide a user-friendly interface to display real-time monitoring data and intelligent scheduling results, and support remote monitoring and decision support. (5) Feedback and iteration: Based on user feedback and actual application effects, the system continuously optimizes algorithms and strategies to improve the accuracy and reliability of the system.

[0028] Embodiment 3

[0029] Based on the first embodiment, Figure 1 As shown in Figure 1, data preprocessing is a key step in data analysis and machine learning, especially when processing sensor network data. Data cleaning, format conversion, and preliminary analysis can significantly improve data quality and provide a reliable foundation for subsequent model training and decision support.

[0030] Algorithm steps:

[0031] (1) Data loading: Obtain data from the sensor network module.

[0032] (2) Data cleaning: Use Z-score or IQR method to eliminate null or invalid values.

[0033] Z-score outlier detection:

[0034]

[0035] Where x is a data point, μ is the mean, and σ is the standard deviation. If |z| is greater than a certain threshold (such as 3), it is considered an outlier. IQR (interquartile range) outlier detection:

[0036] IQR=Q3-Q1

[0037] Among them, Q3 is the third quartile and Q1 is the first quartile. Data points falling outside [Q1-1.5×IQR,Q3+1.5×IQR] are considered outliers.

[0038] (3) Format conversion: Convert data into a unified format (such as converting timestamps into a standard format).

[0039] (4) Preliminary analysis: calculate statistics (such as mean, standard deviation, maximum value, minimum value, etc.).

[0040] (5) Noise removal: Use moving average filtering method to reduce noise.

[0041] Moving average filtering:

[0042]

[0043] Among them, y t is the filtered data point, x t-i is the original data point, and k is the window size.

[0044] The execution code is provided below:

[0045] import pandas as pd

[0046] import numpy as np

[0047] from scipy import stats

[0048] #Step 1: Data loading

[0049] #Assume the sensor data is already stored in a CSV file

[0050] data=pd.read_csv('sensor_data.csv')

[0051] #Step 2: Data cleaning

[0052] #Remove empty or invalid values

[0053] data.dropna(inplace=True)

[0054] #Use Z-score to remove outliers

[0055] z_scores=np.abs(stats.zscore(data.select_dtypes(include=[np.number])))

[0056] data=data[(z_scores<3).all(axis=1)]

[0057] #Or use IQR to remove outliers

[0058] #Q1 = data.quantile(0.25)

[0059] #Q3 = data.quantile(0.75)

[0060] #IQR=Q3-Q1

[0061] #data=data[~((data<(Q1-1.5*IQR))|(data>(Q3+1.5*IQR))).any(axis=1)]#Step 3: Format conversion

[0062] #Assume the timestamp column is named 'timestamp' and convert it to datetime format

[0063] data['timestamp']=pd.to_datetime(data['timestamp'])

[0064] #Step 4: Preliminary Analysis

[0065] #Calculate statistics

[0066] descriptive_stats=data.describe()

[0067] print("Descriptive Statistics:\n",descriptive_stats)

[0068] #Step 5: Noise Removal

[0069] #Use moving average filtering to reduce noise

[0070] window_size=3#window size

[0071] data_filtered = data.copy()

[0072] for column in data.select_dtypes(include=[np.number]).columns:

[0073] data_filtered[column]=data[column].rolling(window=window_size,center=True).mean()

[0074] #Remove NaN values ​​caused by window size

[0075] data_filtered.dropna(inplace=True)

[0076] # Output the processed data

[0077] print("ProcessedData:\n",data_filtered.head())

[0078] Embodiment 4

[0079] Based on the first embodiment, Figure 1 As shown,

[0080] The fault warning module is a key component in the industrial Internet of Things and intelligent manufacturing. It uses machine learning algorithms to monitor and predict the operating status of equipment in real time, so as to promptly detect abnormalities or potential fault trends and trigger warning signals.

[0081] Algorithm steps

[0082] (1) Data collection: Collect time series data from device sensors.

[0083] (2) Data preprocessing: clean data, handle missing values, and perform feature engineering.

[0084] (3) Model training: Use the ARIMA time series prediction algorithm to train the model.

[0085] ARIMA model: ARIMA (p, d, q) model is a method in time series analysis, where p is the order of the autoregressive term, d is the difference order, and q is the order of the moving average term. The formula of the ARIMA model is:

[0086] φ(B)△ d y t =θ(B)∈ t

[0087] Among them, φ(B) and θ(B) are the polynomials of autoregression and moving average, respectively, and Δ d is the d-order difference, y t is the time series data, ∈ t It's white noise.

[0088] (4) Real-time monitoring: Monitor the equipment operating status in real time and calculate the difference between the predicted value and the actual value.

[0089] (5) Anomaly detection: Set a threshold to determine whether the difference between the predicted value and the actual value exceeds the threshold to detect anomalies. Set a threshold τ. If the predicted value With the actual value y t If the difference between exceeds the threshold, an abnormality is considered:

[0090]

[0091] (6) Warning signal triggering: Once an abnormality is detected, the warning signal is triggered immediately.

[0093] (7) Notify relevant personnel: Inform relevant personnel via SMS, email or system notification.

[0094] The execution code is given below:

[0095]

[0096]

[0097]

[0098] #Note: The code examples here are for demonstration purposes only. In actual applications, more details need to be handled, such as error handling, logging, model retraining, etc.

[0099] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent monitoring system for coal mine electromechanical transportation based on the Internet of Things, including a data acquisition and preprocessing module based on the system architecture, a real-time monitoring and early warning module, and a remote operation and management module, characterized in that: The data acquisition and preprocessing module adopts a layered architecture design, which includes a data acquisition layer, a data transmission layer and a data preprocessing layer. The data acquisition layer is responsible for obtaining raw data from the sensor network; The data transmission layer ensures the safe and efficient transmission of data; the data preprocessing layer cleans, converts the format and performs preliminary analysis on the data; the data acquisition and preprocessing module is divided into multiple independent but cooperative components, including sensor interface components, data cache components, data cleaning components, etc., to facilitate the expansion and maintenance of the system; the functions of the data acquisition and preprocessing module include: (1) Real-time data acquisition: real-time collection of various physical parameters of coal mine electromechanical transportation equipment through the sensor network; (2) Data transmission and encryption: ensure that the collected data can be safely and quickly transmitted to the system center, and use appropriate encryption technology to protect data security; (3) Data preprocessing: clean the raw data, remove outliers and noise, and perform format conversion to provide a high-quality data foundation for subsequent analysis; The real-time monitoring and early warning module adopts an event-driven design mode. When receiving data or trigger conditions from the data acquisition and preprocessing module, it automatically performs corresponding monitoring and early warning operations; it includes a highly customizable graphical user interface for real-time display of equipment operation status and early warning information; the functions of the real-time monitoring and early warning module include: (1) real-time monitoring: displaying the real-time operation status of the equipment in a graphical manner, including position, speed, and load information; (2) historical data playback: supporting the query and playback of historical data, which is convenient for management personnel to conduct post-analysis and decision-making; (3) fault early warning: using machine learning algorithms to monitor and predict the equipment operation status in real time, and immediately triggering a warning signal once an abnormality or potential fault trend is found; The remote operation and maintenance and management module adopts a microservice architecture design, dividing the module into fault diagnosis services, operation and maintenance management services, etc., to facilitate system expansion and upgrading; it contains a data warehouse for storing and analyzing historical data and operation and maintenance records of equipment, and uses big data analysis technology to predict faults and optimize operation and maintenance; the functions of the remote operation and maintenance and management module include: (1) Remote fault diagnosis: using big data analysis technology and expert systems to conduct in-depth analysis of equipment data, identify fault types and causes, and provide remote fault diagnosis reports and recommended maintenance plans; (2) Operation and maintenance management: integrating equipment maintenance records, maintenance plans, and spare parts inventory information to realize the information management of equipment maintenance work, including maintenance task allocation, progress tracking, and spare parts procurement; (3) Report generation and sharing: automatically generating operation and maintenance reports and fault diagnosis reports, supporting export and sharing functions, to facilitate management personnel to make decisions and communicate.

2. According to the Internet of Things-based coal mine electromechanical transportation intelligent monitoring system of claim 1, it is characterized by: The data acquisition and preprocessing module functionally includes a sensor network module and a data preprocessing module. The sensor network module is deployed at various key parts of the electromechanical transportation equipment of the coal mine, and is used to collect a variety of physical parameters including equipment vibration, temperature, pressure, speed, etc. in real time, and transmit these data to the system center through the Internet of Things communication protocol after the data are collected; The data preprocessing module receives data from the sensor network module, performs data cleaning, format conversion and preliminary analysis, removes abnormal data and noise, and provides an accurate data basis for subsequent data analysis and fault diagnosis.

3. According to the Internet of Things-based intelligent monitoring system for coal mine electromechanical transportation according to claim 1, it is characterized by: The real-time monitoring and early warning module functionally includes a real-time monitoring display module and a fault early warning module. The real-time monitoring display module displays the real-time operating status of coal mine electromechanical transportation equipment in a graphical interface, including the location, speed, and load information of each equipment, and supports historical data playback, which is convenient for management personnel to monitor and dispatch in real time; the fault early warning module uses a machine learning algorithm to monitor and predict the equipment operating status in real time based on the data provided by the data preprocessing module. Once an abnormality or potential fault trend is found, a warning signal is triggered immediately, and relevant personnel are informed through SMS, email or system notification.

4. According to the Internet of Things-based coal mine electromechanical transportation intelligent monitoring system of claim 1, it is characterized by: The remote operation and maintenance and management module functionally includes a remote fault diagnosis module and an operation and maintenance management module. The remote fault diagnosis module uses big data analysis technology and expert systems to conduct in-depth analysis of the collected equipment data, identify the type and cause of equipment failure, and provide remote fault diagnosis reports and recommended maintenance plans; the operation and maintenance management module integrates equipment maintenance records, maintenance plans, and spare parts inventory information to achieve information management of equipment maintenance work, including maintenance task allocation, progress tracking, and spare parts procurement, thereby improving operation and maintenance efficiency and resource utilization.

5. According to the Internet of Things-based coal mine electromechanical transportation intelligent monitoring system of claim 1, it is characterized by: The data acquisition and preprocessing module transmits data with the sensor network through a standard Internet of Things communication protocol, and passes the preprocessed data to the real-time monitoring and early warning module through an internal interface or a message queue for further analysis and display.

6. The coal mine electromechanical transportation intelligent monitoring system based on the Internet of Things according to claim 1 is characterized by: The real-time monitoring and early warning module receives the pre-processed data, performs real-time monitoring and early warning analysis, and transmits the early warning information to the remote operation and management module through an internal interface or a message queue for remote fault diagnosis and operation and maintenance management. It interacts with the user through a GUI, displays real-time monitoring data and early warning information, and receives user instructions and input.

7. The intelligent monitoring system for coal mine electromechanical transportation based on the Internet of Things according to claim 1 is characterized by: The remote operation and maintenance and management module receives warning information, performs remote fault diagnosis and operation and maintenance management, indirectly obtains equipment data through the real-time monitoring and warning module for fault analysis and operation and maintenance optimization, interacts with users through a web interface or mobile application, displays operation and maintenance reports, fault diagnosis results and operation and maintenance management information, and receives user instructions and input.

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