Method and system for early predicting and warning about mine disaster by field monitoring

Abstract

To provide a method and system for early predicting and warning about a mine disaster by field monitoring.SOLUTION: In the method, sensors are installed at a mine site to monitor surface water and ground water data, and underground rock data in real time, so that mine security management staff can acquire timely and overall data for better understanding of a state of a mine environment. A prediction analysis module analyzes the surface water and the ground water data and the underground rock data by using deep digging and a computational learning technique to predict a risk evaluation index Fpzs, is useful for prediction of a potential disaster risk, comprehensively considers various factors such as the number of past disasters Lzs, a water level variation coefficient Sbxs, and a rock stability coefficient Ywxs, is useful for quantization of a risk level, and provides more accurate disaster risk evaluation to allow for reduction of casualties due to mine disasters.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to the technical field of mine disasters, and more particularly to a method and system for early prediction and warning of mine disasters through on-site monitoring. [Background technology]

[0002] In the mining industry, safety is one of the most important issues, and mine disasters usually include flood damage caused by rising groundwater levels, landslides, mudslides, debris flows, etc. caused by rock stability issues. These disasters can pose serious threats not only to the safety of mining facilities and equipment, but also to the life safety of workers and the environment. Therefore, an effective monitoring and prediction system is needed to identify potential hazardous situations in advance for mining operations, take appropriate preventive measures, and ensure the sustainability and safety of mine operations.

[0003] With the development of science and technology, traditional mine prediction systems are increasingly relying on information and intelligent means. However, traditional prediction methods for data monitoring always monitor and collect data information on the mine surface, but do not collect and monitor data on the mine underground. Many factors such as the water level change of the mine underground water source, the strength of underground rocks, pore size, temperature difference and crack depth affect the frequency of disasters in the entire mine, so how to more accurately predict the risk assessment results of the mine underground is the problem to be solved by the present invention. Summary of the Invention [Means for solving the problem]

[0004] SUMMARY OF THE DISCLOSURE In view of the shortcomings of the prior art, the present invention provides a mine disaster early prediction and warning method and system based on on-site monitoring, and solves the problems in the background art mentioned above.

[0005] In order to achieve the above objectives, the present invention is realized by the following technical means, which is a mine disaster early prediction and warning system based on on-site monitoring, comprising: a data monitoring module, a data collection module, a prediction analysis module, an early warning notification module, and a maintenance management module; The data monitoring module is used to place sensors at suitable locations on-site and monitor the surface water and groundwater data and underground rock data of the mine in real time; The data collection module is used to collect and organize data information transmitted from sensors, process and convert the data, convert the data into digital signals, and transmit the data set in a centralized manner; The prediction analysis module digs into the mine's surface water and groundwater data and underground rock data, and obtains the rock permeability factor Ystz, the float factor Fdyz, the water level fluctuation coefficient Sbxs and the rock stability coefficient Ywxs through computational analysis, and then inputs them into a data set for computational learning to predict and obtain the risk assessment index Fpzs, which is obtained by the following formula:

[0006]

number

[0007] In the formula, Lzs represents the number of past disasters, and f 1 represents the weight value of the water level fluctuation coefficient Sbxs, and f 2 represents the rock stability coefficient Ywxs, where f 1 +f 2 ≦1.0, R represents the correction factor, Relate the pore size Kjz to the temperature difference Wdc to obtain the rock permeability factor Ystz, relate the original water level Ysw to the current water level Xsw to obtain the float factor Fdyz, relate the rock permeability factor Ystz to the float factor Fdyz to obtain the water level variation coefficient Sbxs, relate the load Hzz to the fracture depth to obtain the rock stability coefficient Ywxs, relate the rock stability coefficient Ywxs to the water level variation coefficient Sbxs to obtain the risk assessment index Fpzs, The early warning notification module is used to extract the mine's surface water and groundwater data, underground rock data and past disaster frequency data every week, month or year in the past time axis, obtain the past data, calculate the average value, obtain the average threshold value Q, and compare and analyze the predicted risk assessment index Fpzs and the average threshold value Q to obtain a disaster early warning strategy; The maintenance management module is used to carry out corresponding adjustment and management operations for the acquired disaster early warning strategy.

[0008] Preferably, the data monitoring module includes a sensor unit and an image recording unit; The sensor unit is used to install optical fiber sensors, rain gauges and raindrop counters at suitable locations on the surface and underground of the mine to monitor underground rock data of the mine, including rock surface or internal strain, temperature, humidity, and whether the fractures are expanding or deforming; The image recording unit is used to install cameras at suitable locations on the surface and underground of the mine and to perform on-site monitoring of the mine's surface water and groundwater data, including surface and groundwater levels, rock size, amount of precipitation, depth of wells near the mine and rainfall velocity, i.e., the number of raindrops falling on the ground in one minute.

[0009] Preferably, the data collection module includes a pre-processing unit and a data integration unit; The pre-processing unit is used to repair missing surface water and groundwater data and underground rock data, fill missing values, smooth the data, detect abnormal data points, and perform data transformation on the data; The data integration unit is used to integrate, merge and manage data from multiple data sources that have passed through the pre-processing unit, to facilitate subsequent analysis, reporting and decision making, and to perform data format conversion, convert into digital signals and unify the data format.

[0010] Preferably, based on the collected underground rock data, a deep learning calculation is performed to obtain a rock permeability factor Ystz, and the rock permeability factor Ystz is obtained by the following formula:

[0011]

number

[0012] In the formula, Kjz represents the pore size, Sdz represents the humidity, Wdc represents the temperature difference, and Jys represents the rainfall rate, where w1, w2, w3, and w4 respectively represent the weight values ​​of the pore size Kjz, humidity Sdz, temperature difference Wdc, and rainfall rate Jys, where 0.35≦w1≦0.65, 0.35≦w2≦0.65, 0.45≦w3≦0.55, 0.55≦w4≦0.75, and w1+w2+w3+w4≧1.0, and C is a correction coefficient, and the floating factor Fdyz is obtained by the following formula:

[0013]

number

[0014] In the formula, Xsz represents the current water level underground, Ysz represents the original water level underground, and Ti represents the time interval.

[0015] Preferably, the rock permeability factor Ystz is related to the float factor Fdyz to obtain a water level fluctuation coefficient Sbxs, and the water level fluctuation coefficient Sbxs is obtained by the following formula:

[0016]

number

[0017] In the formula, Jyl represents the amount of precipitation, Sjs represents the well depth, and α1, α2, α3 and α4 represent the weight values ​​of the rock permeability factor Ystz, the float factor Fdyz, the amount of precipitation Jyl and the well depth Sjs, respectively, where X represents the correction coefficient.

[0018] Preferably, the rock stability coefficient Ywxs is obtained by associating the load Hzz with the depth Lfsd of the crack, and the rock stability coefficient Ywxs is obtained by the following formula:

[0019]

Equation

[0020] In the formula, Yqd represents the strength of the rock, and b 1 represents the weight value of the load Hzz, and b 2 represents the weight value of the rock strength Yqd. Here, b 1 +b 2 ≤1.0, and L represents a correction coefficient.

[0021] Preferably, the floating factor Fdyz is compared and analyzed with the preset floating threshold K to obtain a comparison result. When the floating factor Fdyz exceeds the preset floating threshold K, that is, when Fdyz > K, it indicates that the change range of the groundwater level has become large and exceeded the preset index, meaning that the groundwater level is in an abnormal state. When the floating factor is below the preset floating threshold K, that is, when Fdyz < K, it means that the change range of the groundwater level is within an acceptable range.

[0022] Preferably, the acquired past data is calculated to obtain an average threshold Q, and the predicted risk assessment index Fpzs is compared and analyzed with the average threshold Q to obtain a disaster early warning strategy. When the risk assessment index Fpzs exceeds the average threshold Q, that is, when Fpzs > Q, it indicates that the current mine is in a high-risk area, meaning that the groundwater level is gradually rising and water inrush may occur at any time. At this time, an emergency red early warning is sent from inside and outside the mine. When the risk assessment index Fpzs is equal to the average threshold Q, that is, when Fpzs = Q, it indicates that there is no obvious abnormal phenomenon in the current mine. At this time, an orange early warning is sent from inside and outside the mine. At this time, the management staff in the mine continue to monitor the risk situation. When the risk assessment index Fpzs is lower than the average threshold Q, that is, when Fpzs < Q, it indicates that the current mine is in a low-risk state. In this case, preventive facilities should be regularly maintained to ensure that they can operate normally.

[0023] Preferably, the maintenance management module includes a feedback unit and a report summary unit. After the feedback unit sends the corresponding early warning notice from the system, it is used to present to the background observation personnel of the mine through the form of a pop-up again whether the final execution result is normal or there is no abnormality. After the report summary unit regularly visits the mine for on-site monitoring, it is used to provide detailed information including main indicators, problem reports, and trend analysis to the mine management and supervision agencies by regularly generating an event drive report.

[0024] Preferably, it is a method for early prediction and warning of mine disasters by on-site monitoring. Step 1: Install sensors at appropriate locations on the mine through the data monitoring module to monitor and obtain surface water and groundwater data, and underground rock data. Step 2: Preprocess and transform the data collected by monitoring through the data collection module to improve the accuracy of subsequent data extraction, and transfer it to the dataset. Step 3: Extract features from the data in the dataset through the prediction analysis module, analyze and calculate the rock permeability factor Ystz, floating factor Fdyz, water level fluctuation coefficient Sbxs, and rock stability factor Ywxs, and further predict the risk assessment index Fpzs through computational learning. Step 4: Compare the average threshold Q obtained from past data with the above risk assessment index Fpzs through the early warning notification module to obtain an early disaster warning strategy. Step 5: Through the maintenance management module, perform subsequent feedback operations after early warning notification, summarize the usually obtained data results, and regularly generate reports. Effect of the Invention

[0025] The mine disaster early prediction and warning method and system based on on-site monitoring provided by the present invention has the following beneficial effects: (1) The mine disaster early prediction and warning system based on on-site monitoring installs sensors at the mine site to monitor surface water, groundwater data, and underground rock data in real time, allowing mine safety management personnel to obtain timely and comprehensive data to better understand the state of the mine environment. The predictive analysis module uses deep drilling and computational learning technology to analyze surface water, groundwater data, and underground rock data to predict the risk assessment index Fpzs, which is helpful in predicting potential disaster risks, and comprehensively takes into account various factors such as the number of past disasters Lzs, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs, which is helpful in quantifying the risk level, providing a more accurate disaster risk assessment, and reducing the casualties caused by mine disasters. (2) The on-site monitoring mine disaster early prediction and warning system uses the rock seepage factor Ystz to comprehensively consider multiple factors and provide information on the water transport capacity inside underground rocks, and predicts the situation of water inrush occurring in the mine underground at a later stage. By calculating the float factor Fdyz, if the float factor Fdyz exceeds the preset float threshold, it may mean the rise of the groundwater level, which is a potential disaster risk signal, and helps the system to timely detect potential disaster problems. The calculation of the rock stability coefficient Ywxs is used to comprehensively consider factors such as rock strength Yqd and load Hzz to evaluate the stability of rock. A high stability coefficient may reveal that the rock has high resistance ability to the external load Hzz, and a low stability coefficient may indicate that problems such as rock collapse and landslides are likely to occur. Anyway, through the calculation and application of the above data, the system can more accurately evaluate the state of the underground environment of the mine and the potential disaster risks, and help take appropriate measures to reduce the risk of disasters. (3) The mine disaster early prediction and warning method based on on-site monitoring obtains the rock permeability factor Ystz, the float factor Fdyz, the water level fluctuation coefficient Sbxs and the rock stability coefficient Ywxs through on-site monitoring, collection, analysis and calculation, and then predicts the risk assessment index Fpzs through computational learning and compares it with the average threshold Q, and finally obtains a disaster early warning strategy and takes corresponding measures. [Brief description of the drawings]

[0026] [Figure 1] FIG. 1 is a block diagram of a mine disaster early prediction and warning system using on-site monitoring according to the present invention. [Diagram 2] 2 is a flowchart of a mine disaster early prediction and warning method through on-site monitoring according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Hereinafter, the technical aspects of the embodiments of the present invention will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and are not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without performing creative labor belong to the scope of protection of the present invention.

[0028] In the mining industry, safety is one of the most important issues, and mine disasters usually include flood damage caused by rising groundwater levels, landslides, mudslides, debris flows, etc. caused by rock stability issues. These disasters can pose serious threats not only to the safety of mining facilities and equipment, but also to the life safety of workers and the environment. Therefore, an effective monitoring and prediction system is needed to identify potential dangerous situations in advance for mining operations, take appropriate preventive measures, and ensure the sustainability and safety of mine operations.

[0029] With the development of science and technology, traditional mine prediction systems are increasingly relying on information and intelligent means. However, traditional prediction methods for data monitoring always monitor and collect data information on the mine surface, but do not collect and monitor data on the mine underground. Many factors such as water level changes in the mine underground water source, strength of underground rocks, pore size, temperature difference and crack depth affect the frequency of disasters in the entire mine, so how to more accurately predict the risk assessment results of the mine underground is the problem to be solved by the present invention.

[0030] Example 1 Referring to FIG. 1, the present invention provides a mine disaster early prediction and warning method and system through on-site monitoring, which includes a data monitoring module, a data collection module, a prediction analysis module, an early warning notification module, and a maintenance management module; The data monitoring module is used to place sensors at appropriate locations on-site to monitor the mine's surface water and groundwater data, as well as underground rock data in real time. The data acquisition module is used to collect and organize the data transmitted from the sensors, process and convert the data, convert it into a digital signal, and transmit it as a data set. The predictive analysis module digs into the mine's surface water and groundwater data, and underground rock data, and obtains the rock permeability factor Ystz, the floating factor Fdyz, the water level variation coefficient Sbxs, and the rock stability coefficient Ywxs through computational analysis, and then inputs them into the data set for computational learning to predict and obtain the risk assessment index Fpzs, which is obtained by the following formula:

[0031]

number

[0032] In the formula, Lzs represents the number of past disasters, and f 1 represents the weight value of the water level fluctuation coefficient Sbxs, and f 2 represents the rock stability coefficient Ywxs, where Lzs represents the number of past disasters, and f1 represents the weight value of the water level fluctuation coefficient Sbxs, and f 2 represents the rock stability coefficient Ywxs, where f 1 +f 2 ≦1.0, R represents the correction factor, Relate the pore size Kjz to the temperature difference Wdc to obtain the rock permeability factor Ystz, relate the original water level Ysw to the current water level Xsw to obtain the float factor Fdyz, relate the rock permeability factor Ystz to the float factor Fdyz to obtain the water level variation coefficient Sbxs, relate the load Hzz to the fracture depth to obtain the rock stability coefficient Ywxs, relate the rock stability coefficient Ywxs to the water level variation coefficient Sbxs to obtain the risk assessment index Fpzs, The early warning notification module extracts the mine's surface water and groundwater data, underground rock data, and past disaster frequency data for each week, month, or year in the past time axis, obtains the past data, calculates the average value, obtains the average threshold value Q, and then compares and analyzes the predicted risk assessment index Fpzs and the average threshold value Q to obtain a disaster early warning strategy. The maintenance management module is used to carry out corresponding adjustment and management operations for the acquired disaster early warning strategy.

[0033] When the system is operating, the data monitoring module and the data collection module can install sensors at the mine site to monitor surface water, groundwater data, and underground rock data in real time. The predictive analysis module uses deep drilling and computational learning technology to analyze the surface water, groundwater data, and underground rock data to predict the risk assessment index Fpzs, and comprehensively considers various factors such as the number of past disasters Lzs, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs, which is helpful in quantifying the risk level and providing a more accurate disaster risk assessment.

[0034] Example 2 Referring to FIG. 1, specifically, the data monitoring module includes a sensor unit and an image recording unit; The sensor unit is used to install optical fiber sensors, rain gauges and raindrop counters at suitable locations on the surface and underground of the mine to monitor the underground rock data of the mine, including rock surface or internal strain, temperature, humidity, and whether the fractures are expanding or deforming; The image recording unit is used to install cameras at suitable locations on the surface and underground of the mine and to perform on-site monitoring of the mine's surface water and groundwater data, including surface and groundwater levels, rock size, amount of precipitation, depth of wells near the mine and rainfall velocity, i.e. the number of raindrops falling on the ground in one minute.

[0035] The data collection module includes a pre-processing unit and a data integration unit; The pre-processing unit is used to repair missing surface water and groundwater data, and underground rock data, fill in missing values, smooth the data, detect abnormal data points, and perform data transformation on the data, ensuring accurate, consistent and useful information is extracted from the collected data, and better supporting mine operation and management.

[0036] The data integration unit is used to integrate, merge and manage data from multiple data sources that have passed through the pre-processing unit, to facilitate subsequent analysis, reporting and decision making, and to perform data format conversion, convert into digital signals and unify the data format.

[0037] In this embodiment, based on the sensor unit and the image recording unit, multi-dimensional data information is collected from the ore, which helps the system to obtain comprehensive mine environment data and provides more comprehensive disaster monitoring.

[0038] Example 3 Referring to FIG. 1, specifically, based on the collected underground rock data, deep learning calculation is performed to obtain the rock permeability factor Ystz, and the rock permeability factor Ystz is obtained by the following formula:

[0039]

number

[0040] In the formula, Kjz represents the pore size, Sdz represents the humidity, Wdc represents the temperature difference, and Jys represents the rainfall rate, where w1, w2, w3, and w4 represent the weight values ​​of the pore size Kjz, humidity Sdz, temperature difference Wdc, and rainfall rate Jys, respectively, where 0.35≦w1≦0.65, 0.35≦w2≦0.65, 0.45≦w3≦0.55, 0.55≦w4≦0.75, w1+w2+w3+w4≧1.0, and C is a correction coefficient; Here, the pore size Kjz refers to the permeability of the mine rocks, which is obtained by monitoring the optical fiber sensor. Humidity Sdz refers to the overall humidity of the rock, and is obtained by monitoring with a humidity sensor. Temperature difference Wdc refers to the change in the temperature difference between day and night in rocks, where an increase in temperature leads to the expansion of rocks and a decrease in temperature leads to the contraction of rocks. These expansion and contraction processes can cause the pores to expand or contract, further affecting the permeability, which was obtained by monitoring the temperature sensor.

[0041] The floating factor Fdyz is calculated by the following formula:

[0042]

number

[0043] In the formula, Xsz represents the current water level underground, Ysz represents the original water level underground, and Ti represents the time interval.

[0044] The rock permeability factor Ystz is related to the float factor Fdyz to obtain the water level fluctuation coefficient Sbxs, which is obtained by the following equation:

[0045]

number

[0046] In the formula, Jyl represents the amount of precipitation, Sjs represents the well depth, and α1, α2, α3 and α4 represent the weight values ​​of the rock permeability factor Ystz, the float factor Fdyz, the amount of precipitation Jyl and the well depth Sjs, respectively, where X represents the correction coefficient.

[0047] Here, the above precipitation Jyl is usually collected by a rain gauge, and the well depth Sjs is usually obtained by monitoring with a depth sounder.

[0048] The load Hzz is related to the fracture depth Lfsd to obtain the rock stability factor Ywxs, which is given by the following equation:

[0049]

number

[0050] In the formula, Yqd represents the strength of rock, and b 1 represents the weight value of the load Hzz, and b 2 represents the weight value of rock strength Yqd, where b 1 +b 2 ≦1.0, where L represents a correction factor.

[0051] Here, the above-mentioned rock strength Yqd is obtained by rock compressive strength testing. The specific operation is to put a rock sample into a pressure testing machine, and then gradually increase the pressure until the rock sample bursts. The testing machine can record the required pressure value, thereby determining the compressive strength of the rock.

[0052] The load Hzz refers to the load of underground utilities, buildings and supporting structures in the mine, which exerts additional stress and gravity load on the rock, affecting its stability, and is obtained by collecting strain gauges.

[0053] In this embodiment, by calculating the floating factor Fdyz, if the floating factor exceeds the preset floating threshold, it may mean the rise of the groundwater level, which is a potential disaster risk signal and helps the system to timely detect potential disaster problems. The calculation of the rock stability coefficient Ywxs comprehensively considers factors such as the strength Yqd and load Hzz of the rock and is used to evaluate the stability of the rock. A high stability coefficient may indicate that the rock has a high resistance ability against the external load Hzz, while a low stability coefficient may indicate that problems such as rock collapse and landslides are likely to occur.

[0054] (Example 4) Referring to FIG. 1, specifically, the floating factor Fdyz and the preset floating threshold K are compared and analyzed to obtain a comparison result. When the floating factor Fdyz exceeds the preset floating threshold K, that is, when Fdyz > K, it indicates that the change range of the groundwater level becomes large and exceeds the preset index, meaning that the groundwater level is in an abnormal state. The system immediately triggers an emergency red early warning. When the floating factor Fdyz is below the preset floating threshold K, that is, when Fdyz < K, it means that the change range of the groundwater level is within an acceptable range.

[0055] The acquired past data is calculated to obtain an average threshold Q, and the predicted risk assessment index Fpzs and the average threshold Q are compared and analyzed to obtain a disaster early warning strategy. When the risk assessment index Fpzs exceeds the average threshold Q, that is, when Fpzs > Q, it indicates that the current mine is in a high-risk area, meaning that the groundwater level is gradually rising and water inrush may occur at any time. At this time, an emergency red early warning is sent from inside and outside the mine, and an emergency evacuation plan is immediately started. The operation and mining work of the mine are immediately stopped, and all mine employees are quickly evacuated from the dangerous area. The groundwater emergency pumping equipment is started to lower the groundwater level and control the water inrush situation. When the risk assessment index Fpzs is equal to the average threshold Q, that is, when Fpzs = Q, it indicates that there is no obvious abnormal phenomenon in the current mine. At this time, an orange early warning is sent from inside and outside the mine. At this time, the management staff in the mine continues to monitor the risk situation. When the risk assessment index Fpzs is lower than the average threshold Q, that is, when Fpzs < Q, it indicates that the current mine is in a low-risk state. In that case, preventive facilities are regularly maintained to ensure that the preventive facilities can operate normally.

[0056] The maintenance management module includes a feedback unit and a report summary unit. After the feedback unit sends the corresponding early warning notification from the system, it is used to present to the background observation personnel of the mine again through the form of a pop-up whether the final execution result is normal or there is no abnormality. After the report summary unit regularly visits the mine for on-site monitoring, it is used to provide detailed information including main indicators, problem reports, and trend analysis to the mine management and supervision agencies by regularly generating event drive reports.

[0057] In this embodiment, the system triggers corresponding early warning strategies including emergency red early warning, orange early warning, and low-risk state early warning based on the comparative analysis of the risk assessment index Fpzs and the average threshold Q to assist the management and maintenance staff to take appropriate actions quickly. When the risk is high, the system triggers an emergency red warning, immediately stops work, takes emergency evacuation, protects the safety of the lives of mine employees, starts the underground groundwater emergency pumping equipment, lowers the underground water level, and controls the water intrusion situation. The system digs deep into past data to calculate various important elements and provides a scientific basis for risk assessment.

[0058] (Embodiment 5) Referring to FIG. 2, specifically, it is a method for early prediction and warning of mine disasters by on-site monitoring, Step 1, installing and monitoring sensors at appropriate locations on the mine through a data monitoring module to obtain surface water and groundwater data, and underground rock data; Step 2, the data collected by monitoring is pre-processed and converted through a data collection module to improve the accuracy of subsequent data extraction, and then transferred to a data set; Step 3: extracting features from the data of the data set through a predictive analysis module, analyzing and calculating the rock permeability factor Ystz, the float factor Fdyz, the water level variation coefficient Sbxs and the rock stability coefficient Ywxs, and predicting the risk assessment index Fpzs through computational learning; Step 4: Compare the average threshold Q obtained from the past data through the early warning notification module with the above risk assessment index Fpzs to obtain a disaster early warning strategy; and step 5, through the maintenance management module, performing subsequent feedback operations after the early warning notification, and generally summarizing the obtained data results and generating reports periodically.

[0059] In this embodiment, through on-site monitoring, surface water and groundwater data, and underground rock data are collected and recorded, and the rock permeability factor Ystz, the float factor Fdyz, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs are obtained through analytical calculation. Furthermore, the risk assessment index Fpzs is predicted through computational learning, and then compared with the average threshold Q, and finally a disaster warning strategy is obtained and corresponding measures are taken.

[0060] For example, a mine safety management platform has implemented a mine disaster early prediction and warning system based on on-site monitoring. Take a mine as an example: After data monitoring and collection, the borehole size Kjz is 0.05, humidity Sdz is 0.65, temperature difference Wdc is 35, rainfall rate Jys is 12 per second, current underground water level Xsz is 500, original underground water level Ysz is 450, time interval Ti is 5, precipitation Jyl is 1500, well depth Sjs is 50 meters; Using the above data, the following calculations were made: The rock infiltration factor Ystz is obtained by the following formula,

[0061]

Number

[0062] The floating factor Fdyz is obtained by the following formula,

[0063]

Number

[0064] The water level fluctuation coefficient Sbxs is obtained by the following formula,

[0065]

Number

[0066] The rock stability coefficient Ywxs is obtained by the following formula,

[0067]

Number

[0068] The risk assessment index Fpzs is obtained by the following formula,

[0069]

Number

[0070] When the average threshold Q is 15, Fpzs < Q, indicating that the current mine is in a low-risk state. In this case, the preventive facilities should be regularly maintained to ensure that they can operate normally.

[0071] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that these embodiments can be altered, modified, substituted, and varied without departing from the principles and spirit of the present invention, within the scope of the present invention as defined by the appended claims and their equivalents.

Claims

1. The system includes a data monitoring module, a data collection module, a predictive analysis module, an early warning notification module, and a maintenance management module; The data monitoring module is used to place sensors at suitable locations on-site and monitor the surface water and groundwater data and underground rock data of the mine in real time; The data collection module is used to collect and organize data information transmitted from sensors, process and convert the data, convert the data into digital signals, and transmit the data set in a centralized manner; The prediction analysis module is used to dig deep into surface water and groundwater data and underground rock data, and obtain the rock permeability factor Ystz, the float factor Fdyz, the water level fluctuation coefficient Sbxs and the rock stability coefficient Ywxs through computational analysis, and then input them into a data set, perform computational learning, and predict and obtain the risk assessment index Fpzs, where the risk assessment index Fpzs is obtained by the following formula: ##EQU00016## In the formula, Lzs represents the number of past disasters, and f １ represents the weight value of the water level fluctuation coefficient Sbxs, and f ２ represents the rock stability coefficient Ywxs, where f 1 +f 2 ≦1.0, R represents a correction coefficient, Associating the pore size Kjz with the temperature difference Wdc to obtain a rock permeability factor Ystz, associating the original water level Ysw with the current water level Xsw to obtain a float factor Fdyz, associating the rock permeability factor Ystz with the float factor Fdyz to obtain a water level variation coefficient Sbxs, associating the load Hzz with the fracture depth to obtain a rock stability coefficient Ywxs, and associating the rock stability coefficient Ywxs with the water level variation coefficient Sbxs to obtain a risk assessment index Fpzs; The early warning notification module is used to extract surface water and groundwater data, underground rock data, and past disaster frequency data of the mine every week, month, or year in the past time axis, obtain past data, calculate the average value, obtain the average threshold value Q, and further compare and analyze the predicted risk assessment index Fpzs with the average threshold value Q to obtain a disaster early warning strategy; A mine disaster early prediction and warning system using on-site monitoring, characterized in that the maintenance management module is used to carry out appropriate adjustment and management work for the acquired disaster early warning strategy.

2. the data monitoring module includes a sensor unit and an image recording unit; The sensor unit is used to install optical fiber sensors, rain gauges and raindrop counters at suitable locations on the surface and underground of the mine to monitor underground rock data of the mine, including rock surface or internal strain, temperature, humidity, and whether the fractures are expanding or deforming; The mine disaster early prediction and warning system with on-site monitoring according to claim 1, characterized in that the image recording unit is used to install cameras at suitable locations on the surface and underground of the mine and to perform on-site monitoring of the mine's surface water and groundwater data, including surface and groundwater levels, rock size, amount of precipitation, depth of wells near the mine and speed of rainfall, i.e., the number of raindrops falling on the ground per minute.

3. The data collection module includes a pre-processing unit and a data integration unit; The pre-processing unit is used to repair missing surface water and groundwater data and underground rock data, fill missing values, smooth the data, detect abnormal data points, and perform data transformation on the data; The mine disaster early prediction and warning system based on on-site monitoring as described in claim 2, characterized in that the data integration unit is used to integrate, merge and manage data from multiple data sources that have passed through the pre-processing unit, facilitate subsequent analysis, reporting and decision-making, and perform data format conversion, convert into digital signals and unify data formats.

4. Based on the collected underground rock data, a deep learning calculation is performed to obtain a rock permeability factor Ystz, and the rock permeability factor Ystz is obtained by the following formula: ##EQU00017## In the formula, Kjz represents the pore size, Sdz represents the humidity, Wdc represents the temperature difference, and Jys represents the rainfall rate, where w1, w2, w3, and w4 respectively represent the weight values ​​of the pore size Kjz, humidity Sdz, temperature difference Wdc, and rainfall rate Jys, where 0.35≦w1≦0.65, 0.35≦w2≦0.65, 0.45≦w3≦0.55, 0.55≦w4≦0.75, and w1+w2+w3+w4≧1.0, and C is a correction coefficient, and the floating factor Fdyz is obtained by the following formula: [0018] The mine disaster early prediction and warning system based on on-site monitoring as claimed in claim 3, characterized in that in the formula, Xsz represents the current water level underground, Ysz represents the original water level underground, and Ti represents the time interval.

5. The rock permeability factor Ystz is related to the float factor Fdyz to obtain a water level fluctuation coefficient Sbxs, which is obtained by the following formula: [0019] In the formula, Jyl represents the amount of precipitation, Sjs represents the depth of the well, and α1, α2, α3 and α4 respectively represent the weight values ​​of the rock permeability factor Ystz, the float factor Fdyz, the amount of precipitation Jyl and the well depth Sjs, where X represents a correction coefficient. The mine disaster early prediction and warning system using on-site monitoring as described in claim 4.

6. The load Hzz is related to the fracture depth Lfsd to obtain a rock stability coefficient Ywxs, which is given by the following formula: [0020] In the formula, Yqd represents the strength of the rock, and b １ represents the weight value of the load Hzz, and b ２ represents the weight value of rock strength Yqd, where b 1 +b 2 6. The mine disaster early prediction and warning system using on-site monitoring according to claim 5, characterized in that L is a correction coefficient, and ≦1.

0.

7. The floating factor Fdyz and the preset floating threshold K are compared and analyzed to obtain the comparison result; When the floating factor Fdyz exceeds the preset floating threshold value K, i.e., Fdyz>K, it indicates that the groundwater level has changed significantly and exceeded the preset index, which means that the groundwater level is in an abnormal state; The mine disaster early prediction and warning system using on-site monitoring as described in claim 6, characterized in that when the floating factor is below the preset floating threshold K, i.e., Fdyz < K, it means that the range of change in the groundwater level is within an acceptable range.

8. Calculate the acquired past data to obtain the average threshold value Q, and compare and analyze the predicted risk assessment index Fpzs and the average threshold value Q to obtain a disaster early warning strategy; When the risk assessment index Fpzs exceeds the average threshold Q, i.e., Fpzs>Q, it indicates that the current mine is in a high-risk zone, which means that the groundwater level will gradually rise and the water inrush phenomenon will occur from time to time. At this time, an emergency red early warning will be sent inside and outside the mine; When the risk assessment index Fpzs is equal to the average threshold Q, that is, Fpzs=Q, it indicates that there is no obvious abnormal phenomenon in the current mine, and at this time, an orange early warning is sent inside and outside the mine, and at this time, the management and maintenance staff in the mine continue to monitor the risk situation. The mine disaster early prediction and warning system using on-site monitoring as described in claim 7, characterized in that if the risk assessment index Fpzs is below the average threshold value Q, i.e., Fpzs<Q, it indicates that the current mine is in a low-risk state, and in that case, the preventive equipment is regularly maintained to ensure that the preventive equipment operates normally.

9. The maintenance management module includes a feedback unit and a report summary unit; The feedback unit is used to present the final execution result to the mine background monitoring personnel through a pop-up form again whether the final execution result is normal or normal after the system sends the corresponding early warning notification; The mine disaster early prediction and warning system with on-site monitoring according to claim 8, characterized in that the report summarization unit is used to provide detailed information including key indicators, problem reports and trend analysis to the mine management and supervision body by periodically visiting the mine to conduct on-site monitoring and then generating event drive reports on a regular basis.

10. Step 1: installing and monitoring sensors at appropriate locations on the mine through a data monitoring module to obtain surface water and groundwater data, and underground rock data; Step 2: data preprocessing and converting the collected data through the monitoring through the data collection module to improve the accuracy of subsequent data extraction, and then transferring it to a data set; Step 3: extracting features from the data of the data set through a predictive analysis module, analyzing and calculating the rock permeability factor Ystz, the float factor Fdyz, the water level variation coefficient Sbxs and the rock stability coefficient Ywxs, and predicting the risk assessment index Fpzs through computational learning; Step 4: comparing the average threshold Q obtained from the past data with the risk assessment index Fpzs through the early warning notification module to obtain a disaster early warning strategy; and step 5, through the maintenance management module, carrying out subsequent feedback work after the early warning notification, and generally summarizing the obtained data results and making reports periodically.

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