Miner fatigue state real-time monitoring and dynamic early warning method

Abstract

The invention discloses a miner fatigue state real-time monitoring and dynamic early warning method, and aims to effectively prevent safety accidents caused by fatigue of miners and improve the safety and production efficiency of mine operation. According to the method, physiological data (electrocardio, galvanic skin, myoelectricity, heart rate and electroencephalogram) and behavior data (such as working postures, activity intensity and the like) of miners are collected in real time through a multi-mode sensing module, and the data are transmitted to an intelligent analysis module. The intelligent analysis module analyzes the fatigue state of the miner through a support vector machine algorithm and judges the fatigue degree of the miner according to a set fatigue threshold value. The real-time monitoring module dynamically adjusts the early warning level according to the analysis result, and when the fatigue state of the miner reaches the early warning standard, the early warning signal module is triggered to remind the miner to have a rest and forbid the miner to continue working. Through the adaptive learning and optimization module, the system can continuously optimize a fatigue state monitoring and early warning mechanism according to historical data, and the monitoring accuracy and the intelligent level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of miner safety management, and specifically refers to a method for real-time monitoring and dynamic warning of miners' fatigue states. Background Art

[0002] Miners work underground for long hours and are extremely vulnerable to fatigue. Fatigue can lead to decreased attention, weakened judgment, slow reaction, and even cause mine accidents.

[0003] Traditional methods for monitoring miners' fatigue mostly rely on subjective evaluations and simple physiological signal monitoring. These methods have problems such as low accuracy and delayed response, and it is difficult to monitor miners' fatigue states in real time and effectively.

[0004] In the prior art, some methods monitor fatigue by wearing devices such as heart rate monitors and blood oxygen monitors, or by using behavior recognition technology. However, these methods often only monitor single physiological signals or behavioral data and lack comprehensive monitoring of miners' multiple physiological and behavioral states.

[0005] In addition, these technologies generally have lags in data processing and cannot automatically adjust the monitoring and warning mechanisms according to real-time data, resulting in miners' fatigue states not being recognized in time and posing potential hazards to mine safety.

[0006] Current miners' fatigue monitoring systems generally adopt statically set thresholds and simple alarm methods, lacking the ability to dynamically adjust the warning level according to actual data. This results in low warning sensitivity and reliability of the system and cannot adapt to the individual differences of miners and the complexity of environmental changes. Due to the lack of real-time processing of data and comprehensive analysis of multi-modal information, traditional monitoring systems often miss the critical points of fatigue states and cannot effectively prevent miners from entering dangerous states or even causing accidents.

[0007] In view of the above technical defects of the prior art, there is an urgent need for a fatigue monitoring system that integrates multi-modal data, can dynamically adjust the warning mechanism, and can provide customized monitoring services according to different working environments and individual differences of miners. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method for real-time monitoring and dynamic warning of miners' fatigue states, which overcomes the problems in the prior art such as the difficulty in real-time monitoring of miners' fatigue states and insufficient warning measures, can effectively prevent safety accidents caused by miners' excessive fatigue, and improve the safety and production efficiency of mine operations.

[0009] To solve the above technical problem, the technical solution provided by the present invention is: A method for real-time monitoring and dynamic warning of miners' fatigue states, including the following steps:

[0010] S1: Collect the physiological data and behavioral data of miners through a multimodal perception module;

[0011] S2: Transmit the collected data to an intelligent analysis module, which includes data preprocessing, feature extraction, and analysis algorithms;

[0012] S3: The intelligent analysis module preprocesses the data and extracts features, and uses the support vector machine algorithm to analyze the data and generate an analysis result;

[0013] S4: The real-time monitoring module compares the analysis result with a preset threshold to dynamically adjust the warning level;

[0014] S5: When the warning standard is reached, trigger the warning signal module, send the warning information to the user interaction interface, prompt the miner not to go underground, and arrange for rest;

[0015] S6: When the warning standard is not reached, allow the miner to go underground for work;

[0016] S7: Receive the data of the real-time monitoring module and the warning signal module through the administrator remote monitoring and data sharing module to achieve remote monitoring and data sharing;

[0017] S8: The regular adaptive learning and optimization module continuously optimizes the system performance according to the collected historical data.

[0018] Preferably, in step S1, the multimodal perception module includes a heart rate monitoring device, an electroencephalogram detection device, and a behavior recognition camera, which are used to monitor the physiological and behavioral characteristics of miners in real time.

[0019] Preferably, in step S2, the intelligent analysis module analyzes the physiological data and behavioral data of miners through the support vector machine algorithm to identify the fatigue state of miners.

[0020] Preferably, in step S3, the real-time monitoring module dynamically monitors the fatigue degree of miners according to the set fatigue threshold and adjusts the warning level.

[0021] Preferably, in step S4, when the warning signal module determines that the fatigue state of the miner reaches the warning standard, it sends a warning message to the user interaction interface, indicating that the miner is not allowed to go underground and arranging for rest.

[0022] Preferably, in step S5, the user interaction interface is used to display the fatigue state and operation guidance of miners and provide fatigue state feedback to miners.

[0023] Preferably, in step S7, the administrator remote monitoring and data sharing module is used to receive miner data in real time, provide an operation interface for mining area management personnel, and support remote monitoring and scheduling management of miner health status.

[0024] Preferably, the step S8 regular adaptive learning and optimization module optimizes the fatigue state monitoring and early warning thresholds through historical data feedback, improving the early warning accuracy and the system's intelligent level.

[0025] The advantages of the present invention compared with the prior art are as follows:

[0026] 1. By integrating a variety of physiological and behavioral data, it can comprehensively monitor the fatigue state of miners, identify fatigue risks in real time, and take effective measures in a timely manner to prevent safety accidents caused by miners' excessive fatigue.

[0027] 2. By dynamically adjusting the early warning level and fatigue criteria, the system can flexibly adjust the early warning strategy according to the physiological and behavioral characteristics of different miners and environmental changes, ensuring that miners receive appropriate work arrangements.

[0028] 3. Through the adaptive learning and optimization module, the system can continuously optimize the fatigue state monitoring algorithm and early warning mechanism according to historical data, improving the monitoring accuracy and intelligent level of the system.

[0029] 4. Through the administrator's remote monitoring and data sharing, the mining area management personnel can monitor the health status of miners in real time, adjust the operation plan in a timely manner, and further improve the safety and production efficiency of the mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is the architecture diagram of the real-time monitoring and dynamic early warning system for miners' fatigue state of the present invention.

[0031] Figure 2 is the data processing flow chart of the intelligent analysis module of the present invention.

[0032] Figure 3 is the early warning flow chart of the real-time monitoring and dynamic early warning module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0033] The following further describes the present invention in detail with reference to the drawings.

[0034] The present invention relates to a method for real-time monitoring and dynamic early warning of miners' fatigue state, aiming to effectively avoid safety accidents caused by miners' fatigue and improve the safety and production efficiency of mine operations.

[0035] Figure 1 shows the flow chart of the method for real-time monitoring and dynamic early warning of miners' fatigue state of the present invention. As Figure 1 shown, the method for real-time monitoring and dynamic early warning of miners' fatigue state of the present invention includes the following steps:

[0036] The present invention collects the physiological data and behavioral data of miners in real time by setting up multi-modal perception devices. The devices include a heart rate monitoring device, an electroencephalogram detection device, a behavior recognition camera, etc. The devices collect data by monitoring the physiological states (such as heart rate, electroencephalogram) and behavioral characteristics (such as working postures, activity intensities, etc.) of miners in real time, and the collected data will be transmitted to the intelligent analysis module through the transmission module for further processing.

[0037] The collected data enters the intelligent analysis module and is preprocessed within this module. The content of the preprocessing includes data cleaning, noise removal, outlier detection, etc. Through this stage, the noise and interference factors in the data can be removed, ensuring the accuracy of the data and providing reliable basic data for subsequent analysis.

[0038] The data after preprocessing will enter the feature extraction stage. In this stage, algorithms are used to extract the fatigue-related features of miners, including the amplitude of heart rate change, electroencephalogram frequency, exercise intensity, etc., as the input data for analysis. The intelligent analysis module uses the support vector machine (SVM) algorithm to judge the fatigue state, analyzes whether the miner is in a fatigue state, and outputs the result.

[0039] The intelligent analysis module transmits the analysis result of the fatigue state to the real-time monitoring module. The real-time monitoring module compares the set fatigue threshold with the analysis result. When it detects that the fatigue degree of the miner is close to or exceeds the set threshold, the warning signal module will trigger an alarm, prompt the miner to rest, and prohibit them from continuing to work. If the fatigue state of the miner does not reach the warning standard, the system will allow them to continue working and conduct regular fatigue state detection.

[0040] The system of the present invention also provides a remote monitoring and data sharing function. The management personnel in the mining area can view the health status data of miners in real time through the remote operation interface. The management personnel can adjust the operation plan based on these data to ensure that accidents caused by fatigue do not occur to miners.

[0041] The present invention also has an adaptive learning and optimization function. As the system runs for a longer time, the system accumulates historical data and continuously optimizes the algorithms for fatigue state monitoring and warning thresholds, thereby improving the accuracy of fatigue monitoring and the intelligent level of the system.

[0042] Through the above implementation scheme, the present invention can accurately monitor the fatigue state of miners in real time, and through the dynamic warning mechanism, remind miners to rest in time to prevent safety accidents caused by fatigue. In addition, through the remote monitoring and data sharing function, the management personnel in the mining area can always master the health status of miners, further improving the safety and production efficiency of mine operations.

[0043] This embodiment is to clearly demonstrate an application example of the present invention. The specific steps and functional descriptions in the embodiments do not impose any limitations on the protection scope of the present invention. Those of ordinary skill in the art can make changes or improvements based on the design principle of the present invention, and all of them fall within the protection scope of the present invention.

[0044] The present invention and its embodiments have been described above. Such a description is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, creatively design a structural mode and an embodiment similar to the technical solution, they shall all fall within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and dynamic early warning of miners' fatigue status, characterized by: The following steps are involved: S1: Collect the physiological and behavioral data of miners through the multimodal perception module; S2: Transmitting the collected data to an intelligent analysis module, which includes data preprocessing, feature extraction and analysis algorithms; S3: The intelligent analysis module preprocesses the data and extracts features, uses the support vector machine algorithm to analyze the data and generate analysis results; S4: The real-time monitoring module dynamically adjusts the warning level based on the comparison of the analysis results with the preset threshold; S5: When the warning standard is reached, the warning signal module is triggered, and the warning information is sent to the user interaction interface, prompting the miners not to go down the mine and arranging them to rest; S6: When the warning standard is not met, miners are allowed to go down to work; S7: Receive data from the real-time monitoring module and the early warning signal module through the administrator remote monitoring and data sharing module to achieve remote monitoring and data sharing; S8: The regular adaptive learning and optimization module continuously optimizes system performance based on the collected historical data.

2. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: The multimodal perception module in step S1 includes a heart rate monitoring device, a brain wave detection device and a behavior recognition camera, which are used to monitor the physiological and behavioral characteristics of miners in real time.

3. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: In step S2, the intelligent analysis module analyzes the physiological data and behavioral data of the miners through a support vector machine algorithm to identify the fatigue state of the miners.

4. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: In step S3, the real-time monitoring module dynamically monitors the fatigue level of the miners according to the set fatigue threshold and adjusts the warning level.

5. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: In step S4, when the warning signal module determines that the fatigue state of the miner reaches the warning standard, it sends a warning message to the user interaction interface, indicating that the miner is not allowed to go down the mine and is arranged to rest.

6. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: The user interaction interface in step S5 is used to display the miner's fatigue status and work instructions, and provide fatigue status feedback to the miner.

7. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: The administrator remote monitoring and data sharing module in step S7 is used to receive miner data in real time, provide an operation interface for mine area managers, and support remote monitoring and scheduling management of miners' health status.

8. A method for real-time monitoring and dynamic early warning of miner fatigue status according to claim 1, characterized in that: In step S8, the regular adaptive learning and optimization module optimizes fatigue status monitoring and early warning thresholds through historical data feedback, thereby improving early warning accuracy and system intelligence level.

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