Non-coal open-pit mine safety risk monitoring and early warning system

Abstract

The present invention relates to a technical field, specifically to a non-coal open-pit mine safety risk monitoring and early warning system, which includes a laser scattering monitoring module, a signal analysis module, a crack simulation analysis module, a risk identification module, a threshold calculation module, an early warning generation module, and an emergency response guidance module. In the present invention, a laser scattering monitoring module based on Mie scattering theory is used to accurately measure the dust concentration in the air, extract dust particle characteristics through fast Fourier transform, further refine dust monitoring, and provide accurate data support for the formulation of dust prevention measures. The application of finite element analysis and fracture mechanics principles enables the crack simulation analysis module to accurately predict crack expansion, greatly improve the scientific nature of rock stability assessment, reduce geological disaster risks, comprehensively analyze real-time environmental parameters and safety thresholds, quickly trigger early warnings, significantly shorten accident early warning time, and improve the accuracy and effectiveness of early warnings.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a safety risk monitoring and early warning system for non-coal open-pit mines. Background Art

[0002] The field of safety monitoring technology involves the use of a series of technologies and systems to monitor, identify and warn of various safety risks in order to protect personnel safety, prevent property loss and maintain environmental safety. In the context of non-coal open-pit mines, open-pit mine operations involve a variety of potential safety risks, including rock slides, collapses, mechanical failures, and harmful gas leaks. The safety monitoring system provides a key safety layer for mining operations by monitoring risk factors in real time. It combines sensor technology, data analysis, communication technology and automated control systems, which can not only detect and warn of impending dangerous situations, but also automatically take measures to prevent accidents to a certain extent, thereby improving the overall safety management efficiency of mines.

[0003] The non-coal open-pit mine safety risk monitoring and early warning system is a comprehensive safety monitoring and early warning solution designed specifically for non-coal open-pit mines. It aims to identify potential safety threats in real time by monitoring the key safety risk factors in the mine environment and operations. The purpose is to improve the safety of mine workers, prevent accidents, and reduce damage to the environment and property. By identifying risks early and warning relevant personnel in a timely manner, the possibility of major accidents is reduced, thereby achieving the effect of protecting life and property, improving production efficiency and complying with relevant safety regulations.

[0004] Traditional systems rely on indirect measurement or relatively primitive direct measurement technology, resulting in insufficient data accuracy and real-time performance, making it difficult to effectively assess occupational health risks. The lack of high-precision crack monitoring and prediction capabilities makes rock stability assessments dependent on empirical judgments, increasing the risk of geological disasters. In terms of risk identification and early warning generation, there is a lack of automation and intelligence, which affects the timeliness and accuracy of early warnings. Emergency response measures lack pertinence and guidance, affecting the ability to effectively respond to accidents, increasing the risk of safety accidents, affecting production efficiency and economic benefits, and may result in significant casualties and property losses. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a non-coal open-pit mine safety risk monitoring and early warning system.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: the non-coal open-pit mine safety risk monitoring and early warning system includes a laser scattering monitoring module, a signal analysis module, a crack simulation analysis module, a risk identification module, a threshold calculation module, an early warning generation module, and an emergency response guidance module;

[0007] The laser scattering monitoring module is based on Mie scattering theory, emits laser of a specific wavelength to penetrate the air to be tested, analyzes the change of scattered light intensity through the laser scattering principle, quantitatively measures the dust concentration in the air, and obtains dust concentration data;

[0008] The signal analysis module analyzes the received photoelectric signal based on the dust concentration data using fast Fourier transform, extracts the size and distribution characteristics of the dust particles, and generates dust characteristic information;

[0009] The crack simulation analysis module is based on finite element analysis and fracture mechanics principles, combined with geological conditions and external load factors, to simulate the crack expansion process in the mining environment and obtain crack expansion prediction information;

[0010] The risk identification module identifies potential safety risk points based on dust characteristic information and crack extension prediction information, and constructs a high-risk area map;

[0011] The threshold calculation module calculates environmental parameters, including safe operation thresholds of wind speed and direction, based on the high-risk area map and in combination with historical risk data, using statistical analysis methods to obtain environmental safety parameter thresholds;

[0012] The warning generation module compares the current environmental parameters with the environmental safety parameter threshold, and once the threshold is exceeded, a warning is immediately triggered to generate a warning signal;

[0013] The emergency response guidance module provides emergency response measures and guidance based on early warning signals and combined with the mine emergency response plan to minimize risk impact and formulate an emergency response plan.

[0014] As a further scheme of the present invention, the dust concentration data includes the average size of dust particles, the most frequently occurring dust particle size, and the standard deviation of the size distribution; the dust characteristic information includes the geometric shape, surface roughness, and optical properties of the dust particles; the crack extension prediction information specifically includes the expected extension time, the distance to the critical structure, and the predicted extension termination position; the high-risk area map specifically indicates the risk level of the designated area, the corresponding monitoring frequency, and preliminary emergency measures recommendations; the environmental safety parameter threshold includes the safe upper limit of dust concentration, the maximum allowable crack extension rate, and the maximum personnel density in the risk area; the early warning signal includes the alarm level, the alarm issuance time, and the location of the affected area; the emergency response plan includes immediate safety measures, emergency evacuation instructions, and shutdown procedures for key equipment.

[0015] As a further solution of the present invention, the laser scattering monitoring module includes a laser emission submodule, a scattered light capture submodule, and a data analysis submodule;

[0016] The laser emission submodule adjusts the laser to emit laser light of a specific wavelength in a predetermined environment according to the parameter setting of Mie scattering theory, ensures that the air volume and path length penetrated by the laser match the requirements, and obtains the original laser scattering data;

[0017] The scattered light capture submodule uses a calibrated photoelectric detection device fixed beside the laser penetration path to capture the change in light intensity after being scattered by dust particles, and records the change in scattered light intensity at different time points to obtain scattered light intensity change data;

[0018] The data analysis submodule uses the laser scattering raw data and the scattered light intensity change data to perform comparative analysis, calculate the ratio of the scattered light intensity change caused by dust particles, and obtain dust concentration data.

[0019] As a further solution of the present invention, the signal analysis module includes a signal collection submodule, a signal conversion submodule, and a feature extraction submodule;

[0020] The signal collection submodule uses a sensor array to collect photoelectric signals scattered by dust particles based on dust concentration data, ensures that the captured signals cover the required dynamic range and frequency range, and obtains a photoelectric signal set;

[0021] The signal conversion submodule uses a digital converter based on the photoelectric signal set to convert the analog photoelectric signal into a digital format, adjusts the sampling frequency and bit depth to minimize information loss, and obtains a digital signal sequence;

[0022] The feature extraction submodule uses discrete Fourier transform to perform frequency domain analysis on the signal based on the digital signal sequence, identifies the frequency components in the signal that represent the size and distribution characteristics of the dust particles, analyzes the frequency domain information, reveals the physical characteristics of the dust particles, and generates dust characteristic information;

[0023] The discrete Fourier transform, according to the formula:

[0024]

[0025] Perform frequency domain analysis on the signal to analyze the frequency domain information, where X[k] is the complex value of the kth frequency point in the frequency domain, A is the signal enhancement coefficient, n is the sampling point index in the time domain, and x[n] is the value of the nth sampling point in the time domain. is a complex exponential function, N is the total number of sampling points of the signal, and k is the sampling point index in the frequency domain.

[0026] As a further solution of the present invention, the crack simulation analysis module includes a geological data collection submodule, a simulation parameter setting submodule, and an extension impact analysis submodule;

[0027] The geological data collection submodule evaluates the initial state of the crack based on the geological data at the mine site and the initial information of the crack, including the location, length and direction of the crack, and obtains the initial evaluation result of the crack;

[0028] The simulation parameter setting submodule uses the finite element analysis method to simulate the crack expansion path and rate under different geological conditions and external loads based on the initial crack assessment results, adjusts the simulation parameters to match the actual situation, and generates crack expansion simulation results;

[0029] The finite element analysis method is according to the formula:

[0030]

[0031] A simulation is performed, in which is the crack growth rate, C and m are constants related to material properties, and F freq is the loading frequency influence coefficient, E env is the environmental impact coefficient, ΔK is the range of stress intensity factor, K cl is the crack closure factor, F shape is the crack shape factor;

[0032] The extension impact analysis submodule analyzes the key factors affecting crack extension, including rock strength, groundwater level and external load, based on the crack extension simulation results, evaluates the influence of the factors on the crack extension rate and path, and obtains crack extension prediction information.

[0033] As a further solution of the present invention, the risk identification module includes a risk factor integration submodule, a risk rating submodule, and a region marking submodule;

[0034] The risk factor integration submodule collects and analyzes dust characteristic information and crack propagation prediction information, and identifies potential risk sources, including areas prone to collapse, high dust concentration areas, and areas of rapid crack propagation, in combination with the mine operation area, historical safety records, and geological reports, to obtain a preliminary risk source list;

[0035] The risk rating submodule performs risk rating on each potential risk point based on the preliminary risk source list, conducts quantitative analysis on the probability of risk occurrence and the potential impact, reveals the priority of the risk point, and generates a risk rating result;

[0036] The area marking submodule uses the risk rating results to visually mark the risk areas on the mine area map, and different levels of risk are represented by different colors or symbols to construct a high-risk area map.

[0037] As a further solution of the present invention, the threshold calculation module includes a risk data integration submodule, a parameter impact analysis submodule, and a threshold setting submodule;

[0038] The risk data integration submodule summarizes the high-risk area map and historical safety accident records, classifies and organizes the data, marks the accident-frequently occurring areas and related environmental parameters, including historical wind speed and direction records, and obtains a risk and environmental parameter association map;

[0039] The parameter impact analysis submodule analyzes the impact of environmental parameters, including wind speed and wind direction, on safety accidents based on the risk and environmental parameter association diagram, reveals key influencing parameters based on the relationship between parameter changes and accident rates, and generates environmental parameter impact analysis records;

[0040] The threshold setting submodule uses the environmental parameter impact analysis record, combined with the operating conditions and safety standards of the mining area, to determine the safe operating range of each environmental parameter and obtain the environmental safety parameter threshold.

[0041] As a further solution of the present invention, the warning generation module includes a parameter real-time monitoring submodule, a threshold comparison submodule, and an alarm activation submodule;

[0042] The parameter real-time monitoring submodule uses a sensor network to continuously monitor key parameters in the mine environment, including wind speed, wind direction, temperature and humidity, and preliminarily screens the data to eliminate noise and obtain the screened real-time environmental parameter data;

[0043] The threshold comparison submodule compares the filtered real-time environmental parameter data with the environmental safety parameter threshold. If the real-time data exceeds any safety threshold, it is immediately identified as an abnormal state, the environmental parameter is marked as out of limit, and an out-of-limit parameter identification result is generated;

[0044] The alarm activation submodule is based on the out-of-limit parameter identification results. If any out-of-limit parameters exist, the early warning mechanism is immediately triggered, including starting the sound alarm, sending early warning information to the manager's mobile device, and issuing emergency information through the mine's internal network to generate an early warning signal.

[0045] As a further solution of the present invention, the emergency response guidance module includes a warning signal analysis submodule, a response measure formulation submodule, and an emergency plan notification submodule;

[0046] The warning signal analysis submodule receives the warning signal, analyzes the emergency level and impact area of ​​the signal, determines the emergency response level to be taken, evaluates the potential risk impact according to the content of the warning, and obtains the warning signal analysis result;

[0047] The response measures formulation submodule customizes emergency response measures for specific risk scenarios based on the early warning signal analysis results and in combination with the mine emergency response plan, including evacuation routes, emergency shelter designation, and rescue resource deployment, and generates emergency response measures;

[0048] The emergency plan notification submodule uses the mine internal communication system to notify all relevant personnel and departments, including miners, safety personnel, and management, based on emergency response measures to establish an emergency response plan.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, a laser scattering monitoring module based on Mie scattering theory is used to accurately measure the dust concentration in the air, and the characteristics of dust particles are extracted through fast Fourier transform, so as to further refine the dust monitoring and provide accurate data support for the formulation of dust prevention measures. The application of finite element analysis and fracture mechanics principles enables the crack simulation analysis module to accurately predict crack propagation, greatly improve the scientific nature of rock stability assessment, reduce the risk of geological disasters, comprehensively analyze real-time environmental parameters and safety thresholds, quickly trigger warnings, significantly shorten the accident warning time, and improve the accuracy and effectiveness of warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0053] Figure 3 This is a flow chart of the laser scattering monitoring module of the present invention;

[0054] Figure 4 It is a flow chart of the signal analysis module of the present invention;

[0055] Figure 5 It is a flow chart of the crack simulation analysis module of the present invention;

[0056] Figure 6 This is a flow chart of the risk identification module of the present invention;

[0057] Figure 7 is a flow chart of a threshold value calculation module of the present invention;

[0058] Figure 8 This is a flow chart of the early warning generation module of the present invention;

[0059] Fig. 9 It is a flow chart of the emergency response guidance module of the present invention. DETAILED DESCRIPTION

[0060] 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 accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0062] Embodiment 1

[0063] See also Figure 1 and Figure 2 ,The safety risk monitoring and early warning system for non-coal open-pit mines includes laser scattering monitoring module, signal analysis module, crack simulation and analysis module, risk identification module, threshold calculation module, early warning generation module, and emergency response guidance module;

[0064] The laser scattering monitoring module is based on Mie scattering theory. It emits laser of a specific wavelength to penetrate the air to be tested, analyzes the change in the intensity of the scattered light through the principle of laser scattering, quantitatively measures the dust concentration in the air, and obtains dust concentration data.

[0065] The signal analysis module uses fast Fourier transform to analyze the received photoelectric signal based on the dust concentration data, extract the size and distribution characteristics of the dust particles, and generate dust characteristic information;

[0066] The crack simulation analysis module is based on finite element analysis and fracture mechanics principles, combined with geological conditions and external load factors, to simulate the crack propagation process in the mining environment and obtain crack propagation prediction information;

[0067] The risk identification module identifies potential safety risk points and constructs a high-risk area map based on dust characteristic information and crack extension prediction information;

[0068] The threshold calculation module calculates environmental parameters, including the safe operation threshold of wind speed and direction, based on the high-risk area map and historical risk data using statistical analysis methods to obtain the environmental safety parameter threshold;

[0069] The warning generation module compares the current environmental parameters with the environmental safety parameter thresholds. Once the thresholds are exceeded, a warning is triggered immediately and a warning signal is generated;

[0070] The emergency response guidance module provides emergency response measures and guidance based on early warning signals and combined with the mine emergency response plan to minimize the impact of risks and formulate emergency response plans.

[0071] Dust concentration data include the average size of dust particles, the most frequently occurring dust particle size, and the standard deviation of size distribution. Dust characteristic information includes the geometry of dust particles, surface roughness, and optical properties. Crack extension prediction information includes the expected extension time, distance to critical structures, and predicted extension termination position. High-risk area maps include specific indicators of the risk level of designated areas, the corresponding monitoring frequency, and preliminary emergency measures recommendations. Environmental safety parameter thresholds include the safe upper limit of dust concentration, the maximum allowable crack extension rate, and the maximum population density in the risk area. Early warning signals include alarm levels, alarm issuance time, and affected area locations. Emergency response plans include immediate safety measures, emergency evacuation instructions, and shutdown procedures for critical equipment.

[0072] In the laser scattering monitoring module, a laser of a specific wavelength is emitted to penetrate the air to be tested based on the Mie scattering theory, and a photoelectric detector is used to capture the change in laser intensity after being scattered by the air, and the dust concentration data is obtained by calculating the attenuation of the light intensity. This module uses the quantitative relationship between the scattered light intensity and the dust particle concentration, and uses a mathematical model for precise calculation, thereby quantitatively measuring the dust concentration in the air, realizing real-time monitoring and recording of dust concentration. The output data includes the average size of dust particles, the most frequently occurring dust particle size, and the standard deviation of its size distribution, providing basic data for subsequent modules.

[0073] In the signal analysis module, based on the collected dust concentration data, the fast Fourier transform (FFT) technology is used to convert the photoelectric signal from time to frequency to extract the size and distribution characteristics of the dust particles. This module calculates the detailed characteristic information of the dust particles, including the geometric shape, surface roughness and optical properties of the particles, by accurately analyzing the frequency domain characteristics of the photoelectric signal, further refining the dimension and depth of dust monitoring and improving the accuracy of risk assessment.

[0074] In the crack simulation analysis module, finite element analysis and fracture mechanics principles are combined, and geological data and external loads are used as input parameters to simulate the crack expansion process in a mining environment. This module adjusts the simulation parameters to match the actual geological conditions, uses software tools to perform numerical simulation of crack expansion, and generates crack expansion prediction information, including expected expansion time, distance to key structures, and predicted expansion termination position, providing a scientific basis for risk assessment, helping to identify potential safety risk points, and providing accurate information for formulating preventive measures.

[0075] In the risk identification module, the dust characteristic information and crack propagation prediction information are comprehensively applied, and the algorithm analysis is used to identify high-risk areas and construct a high-risk area map. Through comparative analysis and risk assessment algorithms, the module classifies and marks the identified risk points according to risk level, monitoring frequency and emergency measures, and generates a high-risk area map with clear specific indicators, which enhances the visual management of risk areas and improves the pertinence and effectiveness of risk warning.

[0076] In the threshold calculation module, based on the high-risk area map and historical risk data, the safe operating threshold of environmental parameters is calculated using statistical analysis methods. Through complex data analysis and model calculations, this module obtains the environmental safety parameter thresholds such as the safe upper limit of dust concentration, the maximum allowable crack growth rate, and the maximum personnel density in the risk area. As the basic parameter of the early warning system, the threshold is of great significance for achieving real-time monitoring and timely early warning.

[0077] In the early warning generation module, the current environmental parameters are compared with the environmental safety parameter thresholds. Once the threshold is exceeded, an early warning is triggered and an early warning signal is generated. This module uses real-time data analysis and threshold comparison algorithms to quickly make judgments and generate early warning signals including the alarm level, alarm issuance time, and location of the affected area, ensuring that relevant personnel can be notified in time to take countermeasures before risks occur, effectively avoiding or reducing accident losses.

[0078] In the emergency response guidance module, specific emergency response measures and guidance are provided based on the early warning signals and the mine emergency response plan. The module analyzes the specific content of the early warning signals, combines the preset emergency response processes and measures, and develops a targeted emergency response plan, including immediate safety measures, emergency evacuation instructions, and shutdown procedures for key equipment, which minimizes the impact of risk events on personnel, equipment, and production, and ensures the continuity and stability of mine safety production.

[0079] See also Figure 2 and Figure 3 ,The laser scattering monitoring module includes a laser emission submodule, a scattered light capture submodule, and a data analysis submodule;

[0080] In a predetermined environment, the laser emission submodule adjusts the laser to emit laser light of a specific wavelength according to the parameter settings of the Mie scattering theory, ensures that the air volume and path length penetrated by the laser match the requirements, and obtains the original laser scattering data;

[0081] In the laser emission submodule, based on the preset parameters of Mie scattering theory and using the laser control algorithm, the laser is set to output laser light of a specific wavelength. The laser is adjusted to 650nm through the wavelength adjustment command to ensure that the air volume and path length penetrated by the laser meet the experimental design requirements. The power is set to 5mW by adjusting the laser power control command to ensure laser intensity and stability, and to generate raw laser scattering data.

[0082] The scattered light capture submodule uses a calibrated photoelectric detection device fixed beside the laser penetration path to capture the changes in light intensity after being scattered by dust particles, record the changes in scattered light intensity at different time points, and obtain the scattered light intensity change data;

[0083] In the scattered light capture submodule, a photoelectric detection algorithm is used, and a photoelectric detector is configured next to the laser penetration path. The detector is activated using a signal capture command to capture changes in light intensity after scattering by dust particles in real time. The recording operation includes setting the detection sensitivity to 0.1 watts per square meter and the sampling frequency to 1 kHz to ensure that the recorded scattered light intensity change data reflects the scattering conditions at different time points and to generate scattered light intensity change data.

[0084] The data analysis submodule uses the laser scattering raw data and the scattered light intensity change data to perform comparative analysis, calculate the ratio of the scattered light intensity change caused by dust particles, and obtain the dust concentration data;

[0085] In the data analysis submodule, the original laser scattering data and the scattered light intensity change data are used, and a comparative analysis algorithm is adopted to calculate the proportion of the scattered light intensity change caused by dust particles through a numerical analysis method. The operation includes applying a data matching command to compare the original scattered light intensity with the light intensity after scattering by dust particles, and using the light intensity attenuation calculation formula to calculate the dust concentration, which includes setting the dust particle scattered light attenuation coefficient to 1.5 and the sampling data analysis cycle to once every 5 seconds to ensure that the dust concentration data is accurately calculated and generated.

[0086] See also Figure 2 and Figure 4 ,The signal analysis module includes a signal collection submodule, a signal conversion submodule, and a feature extraction submodule;

[0087] The signal collection submodule uses a sensor array to collect photoelectric signals scattered by dust particles based on dust concentration data, ensuring that the captured signals cover the required dynamic range and frequency range, and obtaining a photoelectric signal set;

[0088] In the signal collection submodule, based on the dust concentration data, the sensor configuration command is used to set the sensor array to ensure that the sensor covers the required dynamic range and frequency range. The dynamic range is set to 0-100% scattered light intensity, and the frequency range is set to 0-1kHz. The data acquisition command is used to start collecting the photoelectric signal after being scattered by dust particles. The acquisition frequency is kept synchronized with the sensor array, and the sampling time is set to 10 times per second to ensure that the photoelectric signal set fully reflects the dust scattering characteristics and generates a photoelectric signal set.

[0089] The signal conversion submodule is based on the photoelectric signal set and applies a digital converter to convert the analog photoelectric signal into a digital format, adjusts the sampling frequency and bit depth to minimize information loss, and obtains a digital signal sequence;

[0090] In the signal conversion submodule, based on the photoelectric signal set, the analog-to-digital converter (ADC) configuration command is used to set the sampling frequency of the digital converter to 2kHz and the bit depth to 16 bits to minimize information loss. The analog photoelectric signal is converted into a digital format using the signal conversion instruction. The filtering algorithm is applied to remove noise during the conversion process. The filtering parameters are set to a low-pass filter with a cutoff frequency of 500Hz to ensure that the digital signal sequence reflects the accurate characteristics of the scattering signal and generates a digital signal sequence.

[0091] The feature extraction submodule uses discrete Fourier transform to perform frequency domain analysis on the signal based on the digital signal sequence, identifies the frequency components in the signal that represent the size and distribution characteristics of the dust particles, analyzes the frequency domain information, reveals the physical characteristics of the dust particles, and generates dust characteristic information;

[0092] Discrete Fourier transform method, according to the formula:

[0093]

[0094] Perform frequency domain analysis on the signal to analyze the frequency domain information, where X[k] is the complex value of the kth frequency point in the frequency domain, reflecting the amplitude and phase information of the frequency point, and is used to represent the characteristics of the processed signal in the frequency domain; A is the signal enhancement coefficient, which is used to increase the amplitude of specific frequency components in the signal for clearer identification and analysis; n is the sampling point index in the time domain, from 0 to N-1; x[n] is the value of the nth sampling point in the time domain, representing the original signal; W(n) is the value of the window function at the nth sampling point, which is used to reduce boundary effects and spectrum leakage and improve the quality of frequency domain analysis; is a complex exponential function, in which the rotation factor is used to convert the time domain signal to the frequency domain; N is the total number of sampling points of the signal, which determines the resolution of DFT; k is the sampling point index in the frequency domain, also from 0 to N-1; R is the frequency resolution adjustment factor, which is used to optimize the frequency resolution of the signal to make the analysis result more accurate;

[0095] The execution process is as follows:

[0096] Choose an appropriate sampling frequency, based on the highest frequency component of the signal, and ensure that the sampling frequency is at least twice that frequency to avoid aliasing;

[0097] Apply a window function to each sampling point before performing DFT on the signal to reduce spectrum leakage;

[0098] Signal enhancement, by multiplying a fixed enhancement factor, increases the amplitude of the frequency components of interest in the signal, thereby improving the identifiability of these frequency components;

[0099] Adjust the frequency resolution by multiplying the frequency resolution adjustment factor to optimize the frequency resolution of the DFT to more accurately identify and analyze the frequency components in the signal;

[0100] Perform DFT calculation: According to the improved formula and the above parameters, perform DFT calculation on the signal to obtain the frequency domain representation X[k].

[0101] By introducing the window function W(n), the signal enhancement coefficient A and the frequency resolution adjustment factor R, the improved DFT formula can improve the recognition accuracy of the specific frequency components of the signal and the overall performance of the frequency domain analysis, so that the formula can further improve the analysis effect when processing signals with complex frequency components and analyzing the size and distribution characteristics of dust particles.

[0102] See also Figure 2 and Figure 5 ,The crack simulation and analysis module includes a geological data collection submodule, a simulation parameter setting submodule, and an ,extension impact analysis submodule;

[0103] The geological data collection submodule evaluates the initial state of the cracks based on the geological data of the mine site and the initial information of the cracks, including the location, length and direction of the cracks, and obtains the initial crack evaluation results;

[0104] In the geological data collection submodule, based on the geological data of the mine site and the initial information of the crack, the geological data acquisition instructions are adopted, the position, length and direction of the crack are input as parameters, and the geological evaluation algorithm is used to analyze the geometric characteristics and geological environment of the crack. The geological evaluation algorithm uses the crack geometric model to calculate the stress state of the crack, including crack opening and extension trend analysis, to ensure the evaluation of the initial state of the crack. The parameter settings include the crack depth error range of ±5%, the direction error of ±2°, and the generation of the initial crack evaluation results.

[0105] The simulation parameter setting submodule uses the finite element analysis method to simulate the crack propagation path and rate under different geological conditions and external loads based on the initial crack assessment results, adjusts the simulation parameters to match the actual situation, and generates crack propagation simulation results;

[0106] Finite element analysis method, according to the formula:

[0107]

[0108] A simulation is performed, in which is the crack growth rate, which indicates the rate of change of crack length with the increase of loading cycles, and is the key output value that needs to be predicted in the simulation; C and m are constants related to material properties, which are determined by crack growth experimental data; F freq is the loading frequency influence coefficient, which reflects the influence of external loading frequency on crack growth rate and is determined by crack growth experiments with varying loading frequency; E env is the environmental influence coefficient, which adjusts the influence of environmental conditions (such as humidity, temperature, etc.) on the crack growth rate and is determined based on the crack growth experimental data under different environmental conditions; ΔK is the range of the stress intensity factor, which is a measure of the driving force of crack growth and its calculation depends on the crack size, external load and mechanical properties of the material; K cl is the crack closure factor, which represents the effect of crack surface contact leading to a reduction in the actual stress intensity factor and can be determined experimentally; F shape is the crack shape factor, which combines the changes in stress concentration caused by different crack shapes.

[0109] The execution process is as follows:

[0110] Parameter collection and determination,First, collect experimental data related to crack growth, including the crack growth behavior of materials under different environmental conditions, cracks of different shapes and different loading frequencies;

[0111] Determination of coefficient values, a series of experiments and numerical simulations were carried out to determine the four coefficients introduced in the improved formula: K cl 、E env 、F shape 、F freq ;

[0112] The improved formula is applied, and the coefficient values ​​obtained from experiments and simulations are substituted into the formula to calculate the crack growth rate under specific conditions.

[0113] By improving the existing formulas and introducing crack closure coefficient, environmental influence coefficient, crack shape factor and loading frequency influence coefficient, the crack extension behavior under actual working conditions can be simulated and predicted more accurately. This not only enhances the adaptability and flexibility of the model, but also provides a more detailed way to evaluate and predict crack extension, which is of great significance for the formulation of crack management and prevention strategies.

[0114] Crack propagation prediction, using the calculated crack growth rate to predict the crack propagation path and rate under actual working conditions

[0115] The extension impact analysis submodule analyzes the key factors affecting crack extension, including rock strength, groundwater level and external load, based on the crack extension simulation results, evaluates the influence of factors on crack extension rate and path, and obtains crack extension prediction information;

[0116] In the extension impact analysis submodule, according to the crack extension simulation results, the influence factor analysis command is used, and the rock strength, groundwater level and external load are input as analysis parameters. The crack impact assessment algorithm is used to analyze the specific impact of these factors on the crack extension rate and path. The crack impact assessment algorithm calculates the contribution of different factors to crack extension. The analysis parameter settings include the groundwater level change range of ±10 meters, the external load change range of ±5kN / m 2 , ensuring a comprehensive assessment of the sensitivity of crack propagation to various influencing factors and generating crack propagation prediction information.

[0117] See also Figure 2 and Figure 6 ,The risk identification module includes the risk factor integration submodule, the risk rating submodule, and the ,region labeling submodule;

[0118] The risk factor integration submodule collects and analyzes dust characteristic information and crack propagation prediction information, and identifies potential risk sources, including areas prone to collapse, high dust concentration areas, and areas with rapid crack propagation, based on the mine operation area, historical safety records, and geological reports, to obtain a preliminary risk source list.

[0119] In the risk factor integration submodule, based on dust characteristic information and crack extension prediction information, a comprehensive analysis command is adopted, the mine operation area, historical safety records and geological reports are input as parameters, and the data fusion algorithm is used to analyze potential risk sources. The data fusion algorithm calculates the areas prone to collapse, high dust concentration areas and areas of rapid crack extension based on the parameter weights. The parameter weight settings include 30% for the operation area, 40% for the historical safety records and 30% for the geological report, to ensure a comprehensive assessment of the potential risk sources of the mine and generate a preliminary list of risk sources.

[0120] The risk rating submodule conducts risk rating for each potential risk point based on the preliminary risk source list, conducts quantitative analysis on the probability of risk occurrence and potential impact, reveals the priority of risk points, and generates risk rating results;

[0121] In the risk rating submodule, based on the preliminary risk source list, the risk assessment command is used to set the risk assessment model parameters including the quantitative indicators of risk probability and potential impact, and the probability impact matrix algorithm is used to rate each potential risk point. The probability impact matrix algorithm calculates the priority of the risk point based on the product of the risk probability and the potential impact. The risk rating model parameter settings include the quantitative standards 1-5 for the probability of risk occurrence and the quantitative standards 1-5 for the potential impact, to ensure that the priority of the risk point is revealed and the risk rating result is generated.

[0122] The area marking submodule uses the risk rating results to visually mark the risk areas on the mine area map. Different levels of risk are represented by different colors or symbols to construct a high-risk area map.

[0123] In the area labeling submodule, the risk rating results are used, and the map labeling command is adopted to set the map visualization parameters including the risk area difference level color and symbol. The visual labeling tool is used to visually label the risk areas on the mine area map. The visual labeling tool assigns different colors or symbols to risk areas of different levels according to the priority in the risk rating results. The map visualization parameter settings include high-risk areas with red color, medium-risk areas with yellow color, and low-risk areas with green color, to ensure the clarity and distinction of the high-risk area map and generate a high-risk area map.

[0124] See also Figure 2 and Figure 7 ,The threshold calculation module includes a risk data integration submodule, a parameter impact analysis submodule, and a threshold setting submodule;

[0125] The risk data integration submodule summarizes the high-risk area map and historical safety accident records, classifies and organizes the data, marks the accident-frequently occurring areas and related environmental parameters, including historical wind speed and direction records, and obtains the risk and environmental parameter correlation map;

[0126] In the risk data integration submodule, based on the high-risk area map and historical safety accident records, the data aggregation command is used, and the input includes historical wind speed and direction records as parameters. The data classification and sorting algorithm is used to analyze the collected information. The data classification and sorting algorithm identifies the accident-frequently occurring areas and the patterns of related environmental parameters. The parameter settings include the minimum and maximum values ​​of the wind speed records and the main directions of the wind direction, ensuring that the accident-frequently occurring areas and their environmental parameters are marked, and generating a risk and environmental parameter association map.

[0127] The parameter impact analysis submodule analyzes the impact of environmental parameters, including wind speed and wind direction, on safety accidents based on the risk and environmental parameter association diagram, and reveals key influencing parameters based on the relationship between parameter changes and accident rates, and generates environmental parameter impact analysis records;

[0128] In the parameter impact analysis submodule, based on the risk and environmental parameter association diagram, the environmental impact assessment command is adopted, and wind speed and wind direction are set as analysis objects. The correlation analysis algorithm is used to explore the impact of environmental parameters on safety accidents. The correlation analysis algorithm calculates the correlation coefficient between environmental parameter changes and accident incidence rates. The parameter analysis settings include wind speed change intervals of 2 m / s and wind direction intervals of 45°, revealing the environmental parameters that have the most significant impact on safety accidents and generating environmental parameter impact analysis records.

[0129] The threshold setting submodule uses the environmental parameter impact analysis records, combined with the operating conditions and safety standards of the mining area, to determine the safe operating range of each environmental parameter and obtain the environmental safety parameter threshold;

[0130] In the threshold setting submodule, the environmental parameter impact analysis records are used, the threshold determination command is adopted, and the operating conditions and safety standards of the mining area are combined as a benchmark. The threshold analysis algorithm is used to determine the safe operating range of each environmental parameter. The threshold analysis algorithm locates the safety boundary based on the relationship diagram between the accident probability and the environmental parameters. The parameter settings include the safety upper limit of the wind speed of 15m per second and the safety range of the wind direction of ±30° as the dominant wind direction, to ensure that the safe operating threshold of each environmental parameter is obtained and the environmental safety parameter threshold is generated.

[0131] See also Figure 2 and Figure 8 ,The warning generation module includes a parameter real-time monitoring submodule, a threshold comparison submodule, and an alarm activation submodule;

[0132] The parameter real-time monitoring submodule uses a sensor network to continuously monitor key parameters in the mine environment, including wind speed, wind direction, temperature and humidity, and preliminarily screens the data to eliminate noise and obtain the screened real-time environmental parameter data;

[0133] In the parameter real-time monitoring submodule, the sensor network is used to continuously monitor the key parameters in the mining environment. The data screening algorithm is adopted to set the threshold range of the screening parameters including wind speed, wind direction, temperature and humidity. The low-pass filter command is used to reduce data noise. The filter cutoff frequency is set to 100 Hz to ensure the acquisition of the filtered real-time environmental parameter data, eliminate the noise caused by environmental interference and equipment errors, and generate the filtered real-time environmental parameter data.

[0134] The threshold comparison submodule compares the filtered real-time environmental parameter data with the environmental safety parameter threshold. If the real-time data exceeds any safety threshold, it is immediately identified as an abnormal state, the environmental parameter is marked as out of limit, and the out-of-limit parameter identification result is generated;

[0135] In the threshold comparison submodule, according to the screened real-time environmental parameter data, the threshold comparison command is used, the environmental safety parameter threshold is input as the comparison benchmark, and the numerical comparison algorithm is used to detect whether the real-time data exceeds the safety threshold. The numerical comparison algorithm calculates the difference between the real-time parameter value and the safety threshold. For any parameter, if the real-time data exceeds the safety threshold, the algorithm immediately identifies the environmental parameter as an over-limit state. The parameter comparison settings include a wind speed over-limit threshold of 15m per second and a wind direction over-limit threshold of ±30° for the dominant wind direction, ensuring timely identification of abnormal conditions and generating over-limit parameter identification results.

[0136] The alarm activation submodule is based on the out-of-limit parameter identification results. If any out-of-limit parameters exist, the early warning mechanism is immediately triggered, including starting the sound alarm, sending the early warning information to the manager’s mobile device, and issuing emergency information through the mine’s internal network to generate an early warning signal;

[0137] In the alarm activation submodule, based on the out-of-limit parameter identification results, the alarm trigger command is used to set the activation conditions including the sound alarm and the content of the warning information. The notification distribution algorithm is used to send warning information to the management personnel's mobile devices, and emergency information is released through the mine's internal network. The notification distribution algorithm determines the alarm level and the priority of the emergency information according to the type and level of the out-of-limit parameters. The alarm trigger setting includes the volume of the sound alarm being 85 decibels. The warning information contains the specific values ​​of the out-of-limit parameters and the recommended emergency measures, ensuring that the warning mechanism is immediately activated and a warning signal is generated in any out-of-limit parameter situation.

[0138] See also Figure 2 and Fig. 9 ,The emergency response guidance module includes the warning signal analysis submodule, the response measures formulation submodule, and the emergency plan notification submodule;

[0139] The early warning signal analysis submodule receives the early warning signal, analyzes the emergency level and impact area of ​​the signal, determines the emergency response level to be taken, evaluates the potential risk impact based on the content of the early warning, and obtains the early warning signal analysis results;

[0140] In the early warning signal analysis submodule, the early warning signal is received and the emergency level analysis command is adopted. The parameters of the input signal include the emergency level and the affected area. The risk assessment model is used. The model evaluates the potential risk impact based on the specific information of the early warning content. The risk assessment model setting includes calculating the emergency response level according to the type and intensity of the early warning signal. The emergency response level is divided into three levels: high, medium and low. It ensures the determination of the emergency response level that needs to be taken and generates the early warning signal analysis results.

[0141] The response measures formulation submodule customizes emergency response measures for specific risk scenarios based on the early warning signal analysis results and combined with the mine emergency response plan, including evacuation routes, emergency shelter designation, and rescue resource deployment, and generates emergency response measures;

[0142] In the response measures formulation submodule, based on the early warning signal analysis results, the measure customization command is adopted, combined with the mine emergency response plan, to customize emergency response measures for specific risk scenarios. The measure customization includes specific operations such as evacuation route planning, emergency shelter designation and rescue resource allocation. The measure customization command is based on the emergency response level and impact area of ​​the analysis results, and sets the shortest safe path algorithm for evacuation routes, emergency shelter capacity and location assessment, and quantitative analysis of rescue resource needs, to ensure that accurate and effective emergency response measures are customized for specific risk scenarios and generate emergency response measures.

[0143] The emergency plan notification submodule uses the mine internal communication system to notify all relevant personnel and departments, including miners, safety personnel, and management, based on emergency response measures to establish an emergency response plan;

[0144] In the emergency plan notification submodule, according to the emergency response measures, the notification distribution command is adopted, and the internal communication system of the mine is used to set the content of the notification and the scope of recipients. The emergency response plan is sent to all relevant personnel and departments using the information release algorithm. The information release algorithm is sent in a targeted manner according to the emergency level of the emergency response measures and the target recipient group, including miners, safety personnel, and management. Settings include priority sorting of emergency information and information encryption to ensure privacy security, ensure efficient and accurate notification to all relevant personnel and departments, and establish an emergency response plan.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Non-coal open-pit mine safety risk monitoring and early warning system, characterized by: The non-coal open-pit mine safety risk monitoring and early warning system includes a laser scattering monitoring module, a signal analysis module, a crack simulation analysis module, a risk identification module, a threshold calculation module, an early warning generation module, and an emergency response guidance module; The laser scattering monitoring module is based on Mie scattering theory, emits laser of a specific wavelength to penetrate the air to be tested, analyzes the change of scattered light intensity through the laser scattering principle, quantitatively measures the dust concentration in the air, and obtains dust concentration data; The signal analysis module analyzes the received photoelectric signal based on the dust concentration data using fast Fourier transform, extracts the size and distribution characteristics of the dust particles, and generates dust characteristic information; The crack simulation analysis module is based on finite element analysis and fracture mechanics principles, combined with geological conditions and external load factors, to simulate the crack expansion process in the mining environment and obtain crack expansion prediction information; The risk identification module identifies potential safety risk points based on dust characteristic information and crack extension prediction information, and constructs a high-risk area map; The threshold calculation module calculates environmental parameters, including safe operation thresholds of wind speed and direction, based on the high-risk area map and in combination with historical risk data, using statistical analysis methods to obtain environmental safety parameter thresholds; The warning generation module compares the current environmental parameters with the environmental safety parameter threshold, and once the threshold is exceeded, a warning is immediately triggered to generate a warning signal; The emergency response guidance module provides emergency response measures and guidance based on early warning signals and combined with the mine emergency response plan to minimize risk impact and formulate an emergency response plan; The crack simulation analysis module includes a geological data collection submodule, a simulation parameter setting submodule, and an expansion impact analysis submodule; The geological data collection submodule evaluates the initial state of the crack based on the geological data at the mine site and the initial information of the crack, including the location, length and direction of the crack, and obtains the initial evaluation result of the crack; The simulation parameter setting submodule uses the finite element analysis method to simulate the crack expansion path and rate under different geological conditions and external loads based on the initial crack assessment results, adjusts the simulation parameters to match the actual situation, and generates crack expansion simulation results; The finite element analysis method is according to the formula: A simulation is performed, in which is the crack growth rate, and is a constant related to material properties, is the loading frequency influence coefficient, is the environmental impact coefficient, is the range of stress intensity factor, is the crack closure coefficient, is the crack shape factor; The extension impact analysis submodule analyzes the key factors affecting crack extension, including rock strength, groundwater level and external load, based on the crack extension simulation results, evaluates the influence of the factors on the crack extension rate and path, and obtains crack extension prediction information.

2. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The dust concentration data includes the average size of dust particles, the most frequently occurring dust particle size, and the standard deviation of the size distribution; the dust characteristic information includes the geometric shape, surface roughness, and optical properties of dust particles; the crack extension prediction information specifically includes the expected extension time, the distance to the critical structure, and the predicted extension termination position; the high-risk area map specifically indicates the risk level of the designated area, the corresponding monitoring frequency, and preliminary emergency measures recommendations; the environmental safety parameter threshold includes the safe upper limit of dust concentration, the maximum allowable crack extension rate, and the maximum personnel density in the risk area; the early warning signal includes the alarm level, the alarm issuance time, and the location of the affected area; the emergency response plan includes immediate safety measures, emergency evacuation instructions, and shutdown procedures for key equipment.

3. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The laser scattering monitoring module includes a laser emission submodule, a scattered light capture submodule, and a data analysis submodule; The laser emission submodule adjusts the laser to emit laser light of a specific wavelength in a predetermined environment according to the parameter setting of Mie scattering theory, ensures that the air volume and path length penetrated by the laser match the requirements, and obtains the original laser scattering data; The scattered light capture submodule uses a calibrated photoelectric detection device fixed beside the laser penetration path to capture the change in light intensity after being scattered by dust particles, and records the change in scattered light intensity at different time points to obtain scattered light intensity change data; The data analysis submodule uses the laser scattering raw data and the scattered light intensity change data to perform comparative analysis, calculate the ratio of the scattered light intensity change caused by dust particles, and obtain dust concentration data.

4. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The signal analysis module includes a signal collection submodule, a signal conversion submodule, and a feature extraction submodule; The signal collection submodule uses a sensor array to collect photoelectric signals scattered by dust particles based on dust concentration data, ensures that the captured signals cover the required dynamic range and frequency range, and obtains a photoelectric signal set; The signal conversion submodule uses a digital converter based on the photoelectric signal set to convert the analog photoelectric signal into a digital format, adjusts the sampling frequency and bit depth to minimize information loss, and obtains a digital signal sequence; The feature extraction submodule uses discrete Fourier transform to perform frequency domain analysis on the signal based on the digital signal sequence, identifies the frequency components in the signal that represent the size and distribution characteristics of the dust particles, analyzes the frequency domain information, reveals the physical characteristics of the dust particles, and generates dust characteristic information; The discrete Fourier transform, according to the formula: Perform frequency domain analysis on the signal to analyze the frequency domain information, where: is the complex value of the kth frequency point in the frequency domain, is the signal enhancement factor, is the sampling point index in the time domain, is the value of the nth sampling point in the time domain, is the complex exponential function, is the total number of sampling points of the signal, k is the sampling point index in the frequency domain, is the value of the window function at the nth sampling point, and R is the frequency resolution adjustment factor.

5. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The risk identification module includes a risk factor integration submodule, a risk rating submodule, and a region marking submodule; The risk factor integration submodule collects and analyzes dust characteristic information and crack propagation prediction information, and identifies potential risk sources, including areas prone to collapse, high dust concentration areas, and areas of rapid crack propagation, in combination with the mine operation area, historical safety records, and geological reports, to obtain a preliminary risk source list; The risk rating submodule performs risk rating on each potential risk point based on the preliminary risk source list, conducts quantitative analysis on the probability of risk occurrence and the potential impact, reveals the priority of the risk point, and generates a risk rating result; The area marking submodule uses the risk rating results to visually mark the risk areas on the mine area map, and different levels of risk are represented by different colors or symbols to construct a high-risk area map.

6. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The threshold calculation module includes a risk data integration submodule, a parameter impact analysis submodule, and a threshold setting submodule; The risk data integration submodule summarizes the high-risk area map and historical safety accident records, classifies and organizes the data, marks the accident-frequently occurring areas and related environmental parameters, including historical wind speed and direction records, and obtains a risk and environmental parameter association map; The parameter impact analysis submodule analyzes the impact of environmental parameters, including wind speed and wind direction, on safety accidents based on the risk and environmental parameter association diagram, reveals key influencing parameters based on the relationship between parameter changes and accident rates, and generates environmental parameter impact analysis records; The threshold setting submodule uses the environmental parameter impact analysis record, combined with the operating conditions and safety standards of the mining area, to determine the safe operating range of each environmental parameter and obtain the environmental safety parameter threshold.

7. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The warning generation module includes a parameter real-time monitoring submodule, a threshold comparison submodule, and an alarm activation submodule; The parameter real-time monitoring submodule uses a sensor network to continuously monitor key parameters in the mine environment, including wind speed, wind direction, temperature and humidity, and preliminarily screens the data to eliminate noise and obtain the screened real-time environmental parameter data; The threshold comparison submodule compares the filtered real-time environmental parameter data with the environmental safety parameter threshold. If the real-time data exceeds any safety threshold, it is immediately identified as an abnormal state, the environmental parameter is marked as out of limit, and an out-of-limit parameter identification result is generated; The alarm activation submodule is based on the out-of-limit parameter identification results. If any out-of-limit parameters exist, the early warning mechanism is immediately triggered, including starting the sound alarm, sending early warning information to the manager's mobile device, and issuing emergency information through the mine's internal network to generate an early warning signal.

8. The non-coal open-pit mine safety risk monitoring and early warning system according to claim 1 is characterized by: The emergency response guidance module includes a warning signal analysis submodule, a response measures formulation submodule, and an emergency plan notification submodule; The warning signal analysis submodule receives the warning signal, analyzes the emergency level and impact area of ​​the signal, determines the emergency response level to be taken, evaluates the potential risk impact according to the content of the warning, and obtains the warning signal analysis result; The response measures formulation submodule customizes emergency response measures for specific risk scenarios based on the early warning signal analysis results and in combination with the mine emergency response plan, including evacuation routes, emergency shelter designation, and rescue resource deployment, and generates emergency response measures; The emergency plan notification submodule uses the mine internal communication system to notify all relevant personnel and departments, including miners, safety personnel, and management, based on emergency response measures to establish an emergency response plan.

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