Intelligent mine management and control platform for mine management

Abstract

The invention discloses an intelligent mine management and control platform for mine management, and relates to the technical field of mine management, a mine is divided into different monitoring areas, detailed classification is carried out according to the personnel density, and the personnel density of the monitoring areas is improved. Key data such as ground stress fluctuation amplitude and communication channel electromagnetic interference index are acquired in real time by using a preset sensor, and a data test model is constructed to evaluate the stability of the intelligent mine management and control platform in combination with a sensor acquisition frequency deviation proportion; according to the evaluation result, the area is divided into different out-of-control risk levels, in-depth stability prediction is carried out on the high-out-of-control risk area, and a corresponding emergency management plan is made; the monitoring precision and the emergency response efficiency of mine operation under complex and extreme environment conditions are remarkably improved, and the possibility of safety accidents and operation interruption caused by out-of-control risks is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mine management, and in particular to an intelligent mine management and control platform for mine management. Background Art

[0002] Mine management refers to the process of comprehensive coordination and control of the production, operation, safety and environment of mining enterprises. The existing intelligent mine management and control mainly uses technical means such as the Internet of Things, big data, and artificial intelligence to achieve real-time monitoring, automated control, and intelligent decision-making of mine operations. Specifically, the intelligent management and control system can remotely monitor and analyze the mining equipment, environment, and production process, timely discover and warn potential safety hazards, optimize production scheduling, and improve resource utilization. However, the mining environment is complex and changeable, including extreme weather (such as heavy rain and heavy snow), geological activities (such as earthquakes and landslides), and other unpredictable environmental factors. Under extreme environmental conditions, the intelligent mine management and control platform may lose communication capabilities or data collection capabilities, resulting in uncontrolled mine operations. After losing monitoring, managers cannot grasp the actual situation of the mine and may miss key emergency opportunities, such as failing to evacuate workers or shut down in time, resulting in serious casualties and equipment damage. Summary of the invention

[0003] The purpose of the present invention is to provide a smart mine management and control platform for mine management to solve the shortcomings of the background technology.

[0004] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a smart mine management and control platform for mine management, comprising a region division module, a data acquisition module, a stability assessment module, a loss of control risk prediction module and an emergency management module;

[0005] Area division module: divide the mine into several monitoring areas, determine the density of personnel in each monitoring area, and divide the monitoring area into densely populated monitoring areas and non-densely populated monitoring areas according to the density of personnel;

[0006] Data acquisition module: for densely populated monitoring areas, the mine environment change data and communication channel electromagnetic interference data within a time period S are acquired in real time through pre-arranged sensors. The mine environment change data is the ground stress fluctuation amplitude, and the communication channel electromagnetic interference data is the communication channel electromagnetic interference index;

[0007] Stability evaluation module: Build a data test model, input the ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel, and the sensor acquisition frequency deviation ratio in the S time period into the data test model, and evaluate the stability of the smart mine management and control platform in the S time period based on the model output results;

[0008] Out-of-control risk prediction module: According to the evaluation results of the stability of the smart mine control platform in the S time period, the densely populated monitoring area is divided into different out-of-control risk prediction levels, which are divided into high out-of-control risk prediction level, general out-of-control risk prediction level and out-of-control risk prediction level;

[0009] Emergency management module: For densely populated monitoring areas with high risk of loss of control, further prediction is made on the stability of the smart mine control platform within the S+1 time period, and corresponding emergency management plans are formulated based on the prediction results.

[0010] Preferably, in the area division module, the entire mine is divided into several grid units, each unit represents an independent monitoring area, and the time interval used for analyzing the personnel density is determined. In each monitoring unit, the number of people appearing in the same time interval is counted, and the calculation expression for the personnel density is: personnel density = number of people in the monitoring unit / area of ​​the monitoring unit; the calculated personnel density is compared with the set personnel density threshold; the area where the personnel density is greater than or equal to the personnel density threshold is designated as a densely populated monitoring area; the area where the personnel density is lower than the personnel density threshold is designated as a non-densely populated monitoring area.

[0011] Preferably, in the data acquisition module, the mine environment change data is the ground stress fluctuation amplitude, and the ground stress fluctuation amplitude is obtained by:

[0012] Strain gauges are arranged in key areas of the mine to obtain real-time ground stress data, which are marked as σ(t). The data should cover the required time period S. The ground stress data σ(t) is decomposed into wavelet coefficients W of different scales by multi-scale wavelet decomposition through wavelet function. σ (a,b), where a is the scale parameter and b is the translation parameter, and the expression is: In the formula, is the wavelet basis function, a controls the scale, b controls the translation, and according to the energy distribution of the wavelet coefficients, the largest wavelet component is selected for signal reconstruction. The selected component is subjected to inverse wavelet transform to reconstruct the denoised ground stress signal σrec(t). The expression is: σrec(t)=∑a∑bW σ In the time period S, the maximum value σmax and the minimum value σmin of the reconstructed signal are calculated, and the calculation expressions are: σmax=maxt ∈S σrec(t);σmin=mint ∈S σrec(t); The ground stress fluctuation amplitude is calculated according to the maximum value σmax and the minimum value σmin of the reconstructed signal obtained by calculation. The expression is: ET = |σmax-σmin|; where ET is the ground stress fluctuation amplitude.

[0013] Preferably, the communication channel electromagnetic interference data is a communication channel electromagnetic interference index, and the method for obtaining the communication channel electromagnetic interference index is:

[0014] The electromagnetic interference signal in the communication channel is monitored in real time by the magnetic field sensor installed on the signal transmission path of the communication channel, and the time series data of the electric field strength E(t) and the magnetic field strength H(t) are recorded; the electric field strength and the magnetic field strength are combined to calculate the comprehensive instantaneous interference strength, which is expressed as: Where I(t) is the comprehensive instantaneous interference intensity, η is the normalization coefficient of the magnetic field intensity, and within the S time period, all instantaneous interference intensities are averaged to calculate the average electromagnetic interference intensity within the S time period. The expression is: Where I is the average electromagnetic interference intensity, T is the total duration of the S time period, the reference interference intensity Iref is set, and the electromagnetic interference index of the communication channel is calculated. The expression is: Where EMIindex is the electromagnetic interference index of the communication channel.

[0015] Preferably, in the stability evaluation module, the method for obtaining the acquisition frequency deviation ratio is:

[0016] Set the ideal acquisition frequency ftarget of the sensor. In the S time period, record the timestamp ti of each actual sampling of the sensor to form a time series t1, t2, ..., tn, where n is the total number of samplings. Calculate the time interval Δti between each sampling. The expression is: Δti = t(+1)-ti; ti is the timestamp of the i-th sampling. Calculate the actual instantaneous acquisition frequency fi according to each sampling interval. The expression is: In the S time period, calculate the average value favg of all instantaneous acquisition frequencies, the expression is: n is the total number of sampling times in the S time period. The absolute deviation Δf between the average actual acquisition frequency and the target acquisition frequency is calculated, and the expression is: Δf = |ftarget-favg|; the acquisition frequency deviation ratio is calculated, and the expression is: Where SX is the acquisition frequency deviation ratio.

[0017] Preferably, the ground stress fluctuation amplitude, the communication channel electromagnetic interference index and the acquisition frequency deviation ratio are normalized, and the stability coefficient of the smart mine management and control platform in the S time period is calculated by the normalized ground stress fluctuation amplitude, the communication channel electromagnetic interference index and the acquisition frequency deviation ratio.

[0018] Preferably, in the out-of-control risk prediction module, according to the evaluation result of the stability of the smart mine control and management platform in the S time period, the stability coefficient of the smart mine control and management platform in the S time period is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the stability coefficient of the smart mine control and management platform in the S time period is compared with the first standard threshold and the second standard threshold respectively;

[0019] If the stability coefficient of the smart mine control platform in the S time period is greater than the second standard threshold, the densely populated monitoring area is classified as a high risk of loss of control prediction level;

[0020] If the stability coefficient of the smart mine control platform is greater than or equal to the first standard threshold and less than or equal to the second standard threshold within the S time period, the densely populated monitoring area is classified as a general out-of-control risk prediction level;

[0021] If the stability coefficient of the smart mine management and control platform within the S time period is less than the first standard threshold, the densely populated monitoring area is classified as a low risk of loss of control prediction level.

[0022] Preferably, in the emergency management module, for the densely populated monitoring area at a high risk of loss of control, the stability of the smart mine control platform in the S+1 time period is further predicted, specifically:

[0023] For densely populated monitoring areas with a high risk of loss of control, that is, the stability coefficient generated in the S time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the stability coefficient generated in the S+1 time period that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold is collected, and a data set is established. The mean and standard deviation of the set are calculated. After analysis, the stability of the smart mine control platform in the S+1 time period is further predicted based on the analysis results, and a corresponding emergency management plan is formulated.

[0024] Preferably, if the mean of the stability coefficient in the data set is greater than or equal to the reference threshold of the mean of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold of the standard deviation of the stability coefficient, the stability coefficient of the region shows a high average level and small fluctuation, indicating that the stability in the S+1 time period is good, and no warning signal is generated at this time;

[0025] If the mean value of the stability coefficient is greater than or equal to the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient of the area is high but the volatility is large, and a secondary warning signal is generated at this time;

[0026] If the mean value of the stability coefficient is less than the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient of the area is low and the volatility is large, and a first-level warning signal is generated;

[0027] If the mean of the stability coefficient is less than the reference threshold of the mean of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold of the standard deviation of the stability coefficient, the mean of the stability coefficient in this area is low but the volatility is small, and a third-level warning signal is generated at this time.

[0028] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0029] 1. The present invention comprehensively improves the accuracy and intelligence level of mine management and control by introducing five modules: regional division, real-time data collection, stability assessment, risk of loss of control and emergency management. By accurately dividing the monitoring area, collecting the amplitude of ground stress fluctuation and electromagnetic interference data of the communication channel in real time, and combining the acquisition frequency deviation ratio, a data test model is constructed to dynamically evaluate the stability of the mine management and control platform, so that managers can identify potential risk of loss of control in advance and implement targeted management measures for areas with different risk levels.

[0030] 2. The present invention further analyzes and predicts areas with high risk of loss of control, formulates differentiated emergency management plans, and significantly improves the emergency response capabilities of mines under extreme environmental conditions. Through detailed early warning levels and real-time adjustment measures, it can not only effectively prevent major accidents, but also optimize resource allocation, reduce the overall cost of mine operations, and ensure the sustainability and safety of mine production. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0032] Figure 1 The present invention is a flow chart of the method.

[0033] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] For examples, see Figure 1 and Figure 2 As shown, a smart mine management and control platform for mine management described in this embodiment includes a region division module, a data acquisition module, a stability assessment module, a loss of control risk prediction module and an emergency management module;

[0036] Area division module: divide the mine into several monitoring areas, determine the density of personnel in each monitoring area, and divide the monitoring area into densely populated monitoring areas and non-densely populated monitoring areas according to the density of personnel;

[0037] Data acquisition module: for densely populated monitoring areas, the mine environment change data and communication channel electromagnetic interference data within a time period S are acquired in real time through pre-arranged sensors. The mine environment change data is the ground stress fluctuation amplitude, and the communication channel electromagnetic interference data is the communication channel electromagnetic interference index;

[0038] Stability evaluation module: Build a data test model, input the ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel, and the sensor acquisition frequency deviation ratio in the S time period into the data test model, and evaluate the stability of the smart mine management and control platform in the S time period based on the model output results;

[0039] Out-of-control risk prediction module: According to the evaluation results of the stability of the smart mine control platform in the S time period, the densely populated monitoring area is divided into different out-of-control risk prediction levels, which are divided into high out-of-control risk prediction level, general out-of-control risk prediction level and out-of-control risk prediction level;

[0040] Emergency management module: For densely populated monitoring areas with high risk of loss of control, further prediction is made on the stability of the smart mine control platform within the S+1 time period, and corresponding emergency management plans are formulated based on the prediction results.

[0041] Among them, in the regional division module, by dividing the mine into several monitoring areas and determining the density of personnel in each monitoring area, the monitoring area is divided into a densely populated monitoring area and a non-densely populated monitoring area according to the density of personnel, specifically:

[0042] The mine’s geographic information system (GIS) collects the mine’s geographic data in real time, including topography, geology, facility locations, roads, ventilation systems, etc.

[0043] GIS tools are used to divide the mine into several monitoring areas based on geographic data. Usually, mines can be naturally divided into different operating areas, ventilation areas, traffic areas, etc. according to functions, terrain characteristics, equipment density, etc.

[0044] The boundaries of each area are determined through GIS analysis to ensure that each area is reasonable in monitoring and management, and the distribution of emergency passages and safe shelters is taken into account.

[0045] Use wireless positioning technologies such as RFID (radio frequency identification), GPS, Wi-Fi, Bluetooth beacons or UWB (ultra-wideband) technology to track and record the location information of personnel within the mine in real time.

[0046] Personnel location data: Collect real-time and historical personnel location information from the mine’s personnel location system (such as RFID, GPS, Wi-Fi, UWB, etc.). This data should include timestamp, personnel ID, location information (latitude and longitude or three-dimensional coordinates), etc.

[0047] Mine geographic data: Obtain the geographic information of the mine, including regional boundaries, facility locations, access and shaft layout, etc. This data usually exists in the form of vector graphics (such as Shapefile, GeoJSON format) or raster graphics (such as DEM, satellite imagery).

[0048] Infrastructure data: including information on the mine’s equipment locations, ventilation systems, emergency passages, shelters, etc.

[0049] Integrate positioning data with geographic data: Combine personnel positioning data with mine geographic data, perform spatial registration on the GIS platform, and ensure that personnel location data is accurately mapped to geographic information.

[0050] Time dimension integration: If you want to analyze the density of people in different time periods, you can integrate the positioning data by time period to ensure that accurate density maps can be generated in different time periods.

[0051] The entire mine is divided into several grid units or polygonal areas, each unit represents an independent monitoring area. The degree of detail of the division depends on the accuracy of the data and the analysis requirements. Common grid sizes can be 5m×5m, 10m×10m, etc.

[0052] Determine the time interval used when analyzing the density of people. For example, you can divide it by hour, day, shift, etc. to analyze the distribution of density of people in different time periods.

[0053] In each monitoring unit, the number of people appearing in the same time interval is counted, and the calculation expression of the density of people is: density of people = number of people in the monitoring unit / area of ​​the monitoring unit;

[0054] On a GIS or data visualization platform, each monitoring unit is color-coded according to the calculated population density. Typically, high-density areas are displayed in red or orange, and low-density areas are displayed in green or blue.

[0055] According to the actual situation of density distribution, the color gradient is adjusted to make the density difference more obvious visually. Other important geographic information of the mine (such as channels, equipment, shelter locations, etc.) is superimposed on the heat map to make the personnel density distribution and mine layout more clearly presented.

[0056] According to the mine's safety regulations, operating specifications and past experience, set a threshold for personnel density. For example, the maximum number of personnel allowed per square meter (such as 2 people / square meter) can be specified as the threshold for personnel density.

[0057] Dense and non-dense area division: Compare the calculated population density with the set population density threshold;

[0058] Crowded monitoring area: Areas with a density of people greater than or equal to the density threshold are designated as densely populated monitoring areas. These areas usually require more intensive monitoring, stricter safety measures, and higher emergency response capabilities.

[0059] Non-intensive monitoring area: Areas with a population density lower than the population density threshold are designated as non-intensive monitoring areas. The monitoring intensity and safety measures in such areas can be appropriately reduced, but basic safety must still be guaranteed.

[0060] On the GIS platform or data visualization platform, use different colors or markings to distinguish between densely populated monitoring areas and non-densely populated monitoring areas. For example, use red to highlight dense areas and blue to highlight non-dense areas. Overlay the regional classification results with other key mine facilities (such as equipment locations, emergency exits, and ventilation systems) to facilitate managers to make comprehensive assessments and decisions.

[0061] Data acquisition module: For densely populated monitoring areas, pre-arranged sensors are used to obtain in real time the mine environment change data and communication channel electromagnetic interference data within a time period S. The mine environment change data is the amplitude of ground stress fluctuations, and the communication channel electromagnetic interference data is the communication channel electromagnetic interference index.

[0062] In order to obtain real-time data on mine environment changes and electromagnetic interference data of communication channels within a time period S in a densely populated monitoring area, it is necessary to accurately and timely collect and transmit relevant data through a sensor network, including determining the type and location of the sensor:

[0063] Humidity Sensors: Used to monitor humidity levels, especially in humid or poorly ventilated areas.

[0064] Gas sensor: used to detect toxic or flammable gases (such as carbon monoxide, methane, oxygen concentration).

[0065] Vibration sensor: used to monitor ground or equipment vibrations, providing early warning of potential geological activity or equipment failure.

[0066] Light sensor: used to detect lighting conditions to ensure sufficient light in emergency situations.

[0067] Electromagnetic Interference (EMI) Monitoring Sensors: Electric Field Sensors: Used to monitor changes in electromagnetic field strength, especially near high-voltage cables or in areas where electronic equipment is concentrated.

[0068] Magnetic field sensor: used to detect magnetic field fluctuations caused by high current devices or magnetic field sources (such as transformers, motors, etc.).

[0069] Sensor layout: Environmental monitoring sensors and EMI monitoring sensors are placed at key points in crowded monitoring areas (such as entrances, main passages, and equipment concentration areas) to ensure coverage without blind spots.

[0070] Connect all sensors to the mine's communication network and transmit data to the central monitoring system via wired (such as industrial Ethernet) or wireless (such as Wi-Fi, ZigBee, LoRa). After the sensor is installed, perform preliminary calibration and testing to ensure that the sensor can accurately collect data and synchronize with the monitoring system.

[0071] Set the appropriate sampling rate according to the characteristics and monitoring requirements of different sensors. For example, environmental sensors (temperature, humidity, gas concentration, etc.) usually sample once per second.

[0072] EMI sensors typically sample at higher frequencies, such as 10 times per second or more, to capture instantaneous electromagnetic fluctuations.

[0073] At the beginning of the S period, all relevant sensors are activated to start collecting data. The sensors transmit the collected data to the central monitoring system in real time through the network to ensure the continuity and real-time nature of the data.

[0074] Set up independent transmission channels or frequency bands for different types of data to prevent data congestion or interference. For example, environmental data and EMI data can be transmitted through different frequency bands or network paths. At the same time, to ensure data security and transmission efficiency, encrypt and compress the data to prevent data from being tampered with or lost during transmission.

[0075] The real-time received data is stored in the database of the central monitoring system, and a regular backup mechanism is set up to prevent data loss. The data is stored according to the data type (environmental data and EMI data), time period, region, etc., to facilitate subsequent query and analysis.

[0076] The mine environment change data is the ground stress fluctuation amplitude, and the ground stress fluctuation amplitude is obtained by:

[0077] Strain gauges are arranged in key areas of the mine to obtain real-time ground stress data, which are marked as σ(t). The data should cover the required time period S. The ground stress data σ(t) is decomposed into wavelet coefficients W of different scales by multi-scale wavelet decomposition through wavelet function. σ (a,b), where a is the scale parameter and b is the translation parameter, and the expression is: In the formula, is the wavelet basis function, a controls the scale, b controls the translation, and according to the energy distribution of the wavelet coefficients, the largest wavelet component is selected for signal reconstruction. The selected component is subjected to inverse wavelet transform to reconstruct the denoised ground stress signal σrec(t). The expression is: In the time period S, the maximum value σmax and the minimum value σmin of the reconstructed signal are calculated, and the calculation expressions are: σmax=maxt ∈S σrec(t);σmin=mint ∈S σrec(t); The ground stress fluctuation amplitude is calculated according to the maximum value σmax and the minimum value σmin of the reconstructed signal obtained by calculation. The expression is: ET = |σmax-σmin|; where ET is the ground stress fluctuation amplitude.

[0078] The larger the amplitude of the ground stress fluctuation, the more drastic the stress change of the underground rock mass during the S time period, reflecting the instability of the geological environment. Therefore, the increase in the amplitude of the ground stress fluctuation indicates that the risk faced by the smart mine management and control platform has increased and the overall stability of the platform has decreased. Such drastic stress changes may indicate potential geological disasters, such as the risk of rock bursts or mine tremors, requiring enhanced monitoring and early warning, and timely response measures to ensure the safe operation of the mine.

[0079] The communication channel electromagnetic interference data is the communication channel electromagnetic interference index, and the method for obtaining the communication channel electromagnetic interference index is:

[0080] The electromagnetic interference signal in the communication channel is monitored in real time by the magnetic field sensor installed on the signal transmission path of the communication channel, and the time series data of the electric field strength E(t) and the magnetic field strength H(t) are recorded; the electric field strength and the magnetic field strength are combined to calculate the comprehensive instantaneous interference strength, which is expressed as: Where I(t) is the comprehensive instantaneous interference intensity, η is the normalization coefficient of the magnetic field intensity, which is used to balance the contribution of the electric field and magnetic field intensity, and is generally adjusted according to the specific environment; in the S time period, all instantaneous interference intensities are averaged, and the average electromagnetic interference intensity in the S time period is calculated, and the expression is: In the formula, is the average electromagnetic interference intensity, T is the total duration of the S time period, and the reference interference intensity Iref is set as a reference value for no interference or low interference. This value can be set based on historical data or industry standards. The electromagnetic interference index of the communication channel is calculated as follows: Where EMIindex is the electromagnetic interference index of the communication channel.

[0081] The larger the electromagnetic interference index of the communication channel, the stronger the electromagnetic interference to the communication channel during the S time period, which indicates that the communication stability of the smart mine management and control platform is worse. A higher electromagnetic interference index may lead to unreliable data transmission, increase the risk of communication delays, data loss or signal interruption, thereby affecting the platform's real-time monitoring and control of mining equipment and environment, and thus reducing the stability and security of the entire system.

[0082] Stability evaluation module: Build a data test model, input the ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel, and the sensor acquisition frequency deviation ratio in the S time period into the data test model, and evaluate the stability of the smart mine management and control platform in the S time period based on the model output results;

[0083] Among them, the method for obtaining the acquisition frequency deviation ratio of the sensor in the S time period is:

[0084] Set the ideal acquisition frequency ftarget of the sensor. In the S time period, record the timestamp ti of each actual sampling of the sensor to form a time series t1, t2, ..., tn, where n is the total number of samplings. Calculate the time interval Δti between each sampling. The expression is: Δti = t(+1)-ti; ti is the timestamp of the i-th sampling. Calculate the actual instantaneous acquisition frequency fi according to each sampling interval. The expression is: In the S time period, calculate the average value favg of all instantaneous acquisition frequencies, the expression is: n is the total number of sampling times in the S time period. The absolute deviation Δf between the average actual acquisition frequency and the target acquisition frequency is calculated, and the expression is: Δf = |ftarget-favg|; the acquisition frequency deviation ratio is calculated, and the expression is: Where SX is the acquisition frequency deviation ratio.

[0085] The larger the acquisition frequency deviation ratio, the more unstable the sensor acquisition frequency is during the S time period, and the more it deviates from the set target frequency. This instability may lead to discontinuity or temporal inconsistency in data acquisition, thus affecting the real-time monitoring and data analysis capabilities of the smart mine management and control platform. As a result, the overall stability of the system is reduced, and key events may be missed or erroneous monitoring and decision-making information may be generated, which in turn affects the safety and operational efficiency of the mine.

[0086] The ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel and the acquisition frequency deviation ratio are normalized, and the stability coefficient of the smart mine management and control platform in the S time period is calculated through the normalized ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel and the acquisition frequency deviation ratio.

[0087] For example, the present invention can use the following formula to calculate the stability coefficient of the smart mine management and control platform within the S time period, and the calculation expression is: Where PD is the stability coefficient, ET is the ground stress fluctuation amplitude, EMIindex is the communication channel electromagnetic interference index, SX is the acquisition frequency deviation ratio, a1, a2, and a3 are the proportional coefficients of the ground stress fluctuation amplitude, the communication channel electromagnetic interference index, and the acquisition frequency deviation ratio, and a2>a3>a1>0.

[0088] Out-of-control risk prediction module: According to the evaluation results of the stability of the smart mine management and control platform within the S time period, the densely populated monitoring area is divided into different out-of-control risk prediction levels, which are divided into high out-of-control risk prediction level, general out-of-control risk prediction level and out-of-control risk prediction level.

[0089] The obtained stability coefficient of the smart mine management and control platform in the S time period is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the stability coefficient of the smart mine management and control platform in the S time period is compared with the first standard threshold and the second standard threshold respectively;

[0090] If the stability coefficient of the smart mine control platform in the S time period is greater than the second standard threshold, it means that the smart mine control platform has poor stability in the S time period and there is a high risk of loss of control. At this time, the densely monitored area is classified as a high risk of loss of control prediction level;

[0091] If the stability coefficient of the smart mine control platform in the S time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the smart mine control platform shows a certain stability in the S time period, but there is a moderate risk of loss of control. At this time, the densely monitored area is classified as a general risk of loss of control prediction level;

[0092] If the stability coefficient of the smart mine management and control platform within the S time period is less than the first standard threshold, it means that the smart mine management and control platform shows good stability within the S time period and the risk of loss of control is low. At this time, the densely populated monitoring area is classified as a low risk of loss of control prediction level.

[0093] High loss of control risk prediction level: For areas classified as high loss of control risk, monitoring and early warning measures should be strengthened, sensor density and collection frequency should be increased, and detailed emergency plans should be formulated.

[0094] General out-of-control risk prediction level: For areas classified as general out-of-control risks, maintain normal monitoring intensity and regularly evaluate the changing trend of the stability coefficient to prevent risk escalation.

[0095] Low risk of loss of control prediction level: For areas classified as low risk of loss of control, routine monitoring measures can be maintained while monitoring for signs of increased risk.

[0096] Emergency management module: For densely populated monitoring areas with high risk of loss of control, further prediction is made on the stability of the smart mine control platform within the S+1 time period, and corresponding emergency management plans are formulated based on the prediction results.

[0097] For densely populated monitoring areas with a high risk of loss of control, that is, the stability coefficient generated in the S time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the stability coefficient generated in the S+1 time period that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold is collected, and a data set is established. The mean and standard deviation of the set are calculated. After analysis, the stability of the smart mine control platform in the S+1 time period is further predicted based on the analysis results, and a corresponding emergency management plan is formulated.

[0098] If the mean of the stability coefficient in the data set is greater than or equal to the reference threshold of the mean of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold of the standard deviation of the stability coefficient, the stability coefficient of the area shows a high average level and small fluctuation, indicating that the stability in the S+1 time period is good. Although it shows a high risk of loss of control prediction level in the S time period, the situation seems to have improved, and no warning signal is generated at this time;

[0099] The emergency management plan includes: continued close monitoring and reduced additional intervention, but the real-time nature of monitoring needs to be ensured.

[0100] If the mean value of the stability coefficient is greater than or equal to the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, although the mean value of the stability coefficient in this area is high, the volatility is large, indicating that there is an unstable trend and potential risk in the S+1 period. The high volatility may indicate that the potential risk of the system losing control is still high, and a secondary warning signal is generated at this time;

[0101] The emergency management plan includes: strengthening monitoring frequency and preparing emergency response measures, including personnel evacuation plans.

[0102] If the mean value of the stability coefficient is less than the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient in this area is low and the volatility is large, indicating that the stability in the S+1 time period is poor and the risk of loss of control is very high. At this time, a first-level warning signal is generated;

[0103] The emergency management plan includes: immediately strengthening emergency response, increasing monitoring density, formulating emergency evacuation plans, and preparing resources and manpower in advance to prevent potential accidents.

[0104] If the mean value of the stability coefficient is less than the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient in this area is low but the volatility is small, indicating that the stability in the S+1 time period is low but stable, and the system still has certain risks, but there is no obvious deterioration trend. At this time, a third-level warning signal is generated;

[0105] The emergency management plan includes: maintaining current monitoring efforts, conducting risk assessments, and formulating targeted intervention measures when necessary to prevent risks from further deteriorating.

[0106] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding management measures according to different warning signal levels.

[0107] In this embodiment, the mine is divided into several monitoring areas, and the density of personnel in each monitoring area is determined, and the monitoring area is divided into a densely populated monitoring area and a non-densely populated monitoring area according to the density of personnel; for the densely populated monitoring area, the mine environment change data and the electromagnetic interference data of the communication channel in the S time period are obtained in real time through pre-arranged sensors, the mine environment change data is the ground stress fluctuation amplitude, and the communication channel electromagnetic interference data is the communication channel electromagnetic interference index; a data test model is constructed, the ground stress fluctuation amplitude, the communication channel electromagnetic interference index and the sensor acquisition frequency deviation ratio in the S time period are input into the data test model, and the stability of the smart mine control platform in the S time period is evaluated according to the model output result; according to the evaluation result of the stability of the smart mine control platform in the S time period, the densely populated monitoring area is divided into different out-of-control risk prediction levels, which are respectively divided into a high out-of-control risk prediction level, a general out-of-control risk prediction level and an out-of-control risk prediction level; for the densely populated monitoring area at the high out-of-control risk prediction level, the stability of the smart mine control platform in the S+1 time period is further predicted, and a corresponding emergency management plan is formulated according to the prediction result. By accurately dividing monitoring areas, monitoring key data in real time, building data testing models to evaluate platform stability, and conducting dynamic predictions and developing emergency plans based on risk levels, the accuracy and responsiveness of mine management can be significantly improved, effectively preventing accidents and ensuring the safety and stability of mine operations.

[0108] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0110] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0114] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0115] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A smart mine control platform for mine management, Features: It includes regional division module, data acquisition module, stability assessment module, out-of-control risk prediction module and emergency management module; Area division module: divide the mine into several monitoring areas, determine the density of personnel in each monitoring area, and divide the monitoring area into densely populated monitoring areas and non-densely populated monitoring areas according to the density of personnel; Data acquisition module: for densely populated monitoring areas, the mine environment change data and communication channel electromagnetic interference data within a time period S are acquired in real time through pre-arranged sensors. The mine environment change data is the ground stress fluctuation amplitude, and the communication channel electromagnetic interference data is the communication channel electromagnetic interference index; Stability evaluation module: Build a data test model, input the ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel, and the sensor acquisition frequency deviation ratio in the S time period into the data test model, and evaluate the stability of the smart mine management and control platform in the S time period based on the model output results; Out-of-control risk prediction module: According to the evaluation results of the stability of the smart mine control platform in the S time period, the densely populated monitoring area is divided into different out-of-control risk prediction levels, which are divided into high out-of-control risk prediction level, general out-of-control risk prediction level and out-of-control risk prediction level; Emergency management module: For densely populated monitoring areas with high risk of loss of control, further prediction is made on the stability of the smart mine control platform within the S+1 time period, and corresponding emergency management plans are formulated based on the prediction results.

2. According to claim 1, a smart mine management and control platform for mine management, Features: In the area division module, the entire mine is divided into several grid units, each unit represents an independent monitoring area, and the time interval used for analyzing the personnel density is determined. In each monitoring unit, the number of people appearing in the same time interval is counted. The calculation expression for personnel density is: personnel density = number of people in the monitoring unit / area of ​​the monitoring unit; the calculated personnel density is compared with the set personnel density threshold; the area with a personnel density greater than or equal to the personnel density threshold is designated as a densely populated monitoring area; the area with a personnel density lower than the personnel density threshold is designated as a non-densely populated monitoring area.

3. According to claim 2, a smart mine management and control platform for mine management, Features: In the data acquisition module, the mine environment change data is the ground stress fluctuation amplitude, and the ground stress fluctuation amplitude is obtained by: Strain gauges are arranged in key areas of the mine to obtain real-time ground stress data, which are marked as σ(t). The data should cover the required time period S. The ground stress data σ(t) is decomposed into wavelet coefficients W of different scales by multi-scale wavelet decomposition through wavelet function. σ (a,b), where a is the scale parameter and b is the translation parameter, and the expression is: In the formula, is the wavelet basis function, a controls the scale, b controls the translation, and according to the energy distribution of the wavelet coefficients, the largest wavelet component is selected for signal reconstruction. The selected component is subjected to inverse wavelet transform to reconstruct the denoised ground stress signal σrec(t). The expression is: In the time period S, the maximum value σmax and the minimum value σmin of the reconstructed signal are calculated, and the calculation expressions are: σmax=maxt ∈S σrec(t);σmin=mint ∈S σrec(t); The ground stress fluctuation amplitude is calculated according to the maximum value σmax and the minimum value σmin of the reconstructed signal obtained by calculation. The expression is: ET = |σmax-σmin|; where ET is the ground stress fluctuation amplitude.

4. According to claim 3, a smart mine management and control platform for mine management, Features: The communication channel electromagnetic interference data is the communication channel electromagnetic interference index, and the method for obtaining the communication channel electromagnetic interference index is: The electromagnetic interference signal in the communication channel is monitored in real time by the magnetic field sensor installed on the signal transmission path of the communication channel, and the time series data of the electric field strength E(t) and the magnetic field strength H(t) are recorded; the electric field strength and the magnetic field strength are combined to calculate the comprehensive instantaneous interference strength, which is expressed as: Where I(t) is the comprehensive instantaneous interference intensity, η is the normalization coefficient of the magnetic field intensity, and within the S time period, all instantaneous interference intensities are averaged to calculate the average electromagnetic interference intensity within the S time period. The expression is: In the formula, is the average electromagnetic interference intensity, T is the total duration of the S time period, the reference interference intensity Iref is set, and the electromagnetic interference index of the communication channel is calculated. The expression is: Where EMIindex is the electromagnetic interference index of the communication channel.

5. According to claim 1, a smart mine management and control platform for mine management, Features: In the stability assessment module, the method for obtaining the acquisition frequency deviation ratio is: Set the ideal acquisition frequency ftarget of the sensor. In the S time period, record the timestamp ti of each actual sampling of the sensor to form a time series t1, t2, ..., tn, where n is the total number of samplings. Calculate the time interval Δti between each sampling. The expression is: Δti = t(+1)-ti; ti is the timestamp of the i-th sampling. Calculate the actual instantaneous acquisition frequency fi according to each sampling interval. The expression is: In the S time period, calculate the average value favg of all instantaneous acquisition frequencies, the expression is: n is the total number of sampling times in the S time period. The absolute deviation Δf between the average actual acquisition frequency and the target acquisition frequency is calculated, and the expression is: Δf = |ftarget-favg|; the acquisition frequency deviation ratio is calculated, and the expression is: Where SX is the acquisition frequency deviation ratio.

6. A smart mine management and control platform for mine management according to claim 5, Features: The ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel and the acquisition frequency deviation ratio are normalized, and the stability coefficient of the smart mine management and control platform in the S time period is calculated through the normalized ground stress fluctuation amplitude, the electromagnetic interference index of the communication channel and the acquisition frequency deviation ratio.

7. A smart mine management and control platform for mine management according to claim 6, Features: In the out-of-control risk prediction module, according to the evaluation result of the stability of the smart mine control platform in the S time period, the obtained stability coefficient of the smart mine control platform in the S time period is compared with the gradient standard threshold, the gradient standard threshold includes the first standard threshold and the second standard threshold, and the first standard threshold is less than the second standard threshold, and the stability coefficient of the smart mine control platform in the S time period is compared with the first standard threshold and the second standard threshold respectively; If the stability coefficient of the smart mine control platform in the S time period is greater than the second standard threshold, the densely populated monitoring area is classified as a high risk of loss of control prediction level; If the stability coefficient of the smart mine control platform is greater than or equal to the first standard threshold and less than or equal to the second standard threshold within the S time period, the densely populated monitoring area is classified as a general out-of-control risk prediction level; If the stability coefficient of the smart mine management and control platform within the S time period is less than the first standard threshold, the densely populated monitoring area is classified as a low risk of loss of control prediction level.

8. The intelligent mine management and control platform for mine management according to claim 1, Features: In the emergency management module, for the densely populated monitoring areas with high risk of loss of control, the stability of the smart mine control platform in the S+1 time period is further predicted, specifically: For densely populated monitoring areas with a high risk of loss of control, that is, the stability coefficient generated in the S time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the stability coefficient generated in the S+1 time period that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold is collected, and a data set is established. The mean and standard deviation of the set are calculated. After analysis, the stability of the smart mine control platform in the S+1 time period is further predicted based on the analysis results, and a corresponding emergency management plan is formulated.

9. A smart mine management and control platform for mine management according to claim 8, Features: If the mean of the stability coefficient in the data set is greater than or equal to the reference threshold of the mean of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold of the standard deviation of the stability coefficient, the stability coefficient of the area shows a high average level and small fluctuation, indicating that the stability in the S+1 time period is good, and no warning signal is generated at this time; If the mean value of the stability coefficient is greater than or equal to the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient of the area is high but the volatility is large, and a secondary warning signal is generated at this time; If the mean value of the stability coefficient is less than the reference threshold value of the mean value of the stability coefficient, and the standard deviation of the stability coefficient is greater than or equal to the reference threshold value of the standard deviation of the stability coefficient, the mean value of the stability coefficient of the area is low and the volatility is large, and a first-level warning signal is generated; If the mean of the stability coefficient is less than the reference threshold of the mean of the stability coefficient, and the standard deviation of the stability coefficient is less than the reference threshold of the standard deviation of the stability coefficient, the mean of the stability coefficient in this area is low but the volatility is small, and a third-level warning signal is generated at this time.

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