Safety monitoring system and method for goaf and computing equipment

Abstract

The invention discloses a safety monitoring system and method for a goaf and a computing device, relates to the technical field of goaf safety monitoring, and solves the technical problem that in the prior art, an inert gas supplementing mechanism is lacked, so that goaf accident preventability is reduced. According to the invention, the sensors are classified to obtain sensor types; environment data in the goaf are collected through a sensor; constructing a reference value of the environment data, determining an initial calibration value based on the reference value, calibrating the environment data through the initial calibration value to obtain environment data after primary calibration, constructing a compensation factor, and performing secondary calibration based on the compensation factor to obtain environment data after final calibration; a dynamic balance model is constructed, and the real-time supplement amount of nitrogen is obtained through the dynamic balance model; judging whether the pressure intensity of the goaf is within a preset normal pressure intensity range or not, and completing the monitoring process; the nitrogen content is supplemented in time, and the possibility of accidents is reduced.

Description

Technical Field

[0001] The invention belongs to the field of goaf area safety monitoring, relates to data processing technology, and specifically provides a safety monitoring system, method and computing equipment for goaf areas. Background Art

[0002] Most of the disasters in goaf areas are caused by the lack of effective monitoring of goaf information, which makes it impossible to grasp the status of goaf in real time, resulting in the failure to make timely warnings for abnormalities in goaf. At present, manual monitoring and bundle pipe monitoring are still the main means of monitoring environmental information in closed goaf areas. Regular manual sampling and chromatographic analysis are used to detect the concentration of harmful gases, and to monitor the temperature and the pressure difference between the inside and outside of the enclosure. The manual monitoring method has a large workload, low measurement frequency, poor continuity, and low credibility of the monitoring results. The bundle pipe monitoring system uses a vacuum pump to pump gas to the bundle pipe detection center, and uses a gas chromatograph to regularly analyze the gas composition and concentration changes. Although the bundle pipe monitoring system can realize automatic monitoring, it still has defects such as long sampling distance, difficult maintenance, and long interval time. In addition, once the bundle pipe leaks, the detection results are unreliable. In summary, the current traditional closed goaf monitoring method has problems such as single information, poor timeliness, and low accuracy. It cannot comprehensively perceive the status of the goaf in real time, and the process of the development of disasters is difficult to grasp, let alone early identification and timely warning before the disaster.

[0003] The prior art (the invention patent application with the publication number CN110985095A) discloses a method for fire prevention and extinguishing in goaf by grouting and nitrogen injection, which comprises: pre-burying a grouting pipeline and a nitrogen injection pipeline in the coal seam to be mined along the mining direction; when the mining distance of the coal mining working face is greater than a predetermined distance, the grouting pipeline starts grouting; when the nitrogen injection port of the nitrogen injection pipeline enters the oxidation temperature rising zone, the nitrogen injection pipeline starts nitrogen injection;

[0004] The existing technology considers the start time of nitrogen injection, but does not consider the duration of nitrogen injection and the gas leakage in the goaf. It is difficult to maintain the dynamic balance of nitrogen in the goaf after nitrogen injection, and lacks an inert gas supplement mechanism, which leads to the technical problem of reduced prevention of goaf accidents.

[0005] At the same time, the sensors used in the existing technology do not take into account the vibration caused by the collapse of the roof inside the goaf and the instability of some structures. Long-term vibration causes the sensor fixing device to loosen, resulting in baseline drift and data measurement errors, which leads to technical problems such as reduced accuracy of measurement data.

[0006] The present invention provides a safety monitoring system, method and computing equipment for goaf areas to solve the above technical problems. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a safety monitoring system, method and computing device for a goaf, which is used to solve the technical problems that the prior art does not consider the duration of nitrogen injection and the air leakage situation in the goaf, it is difficult to maintain the dynamic balance of nitrogen in the goaf after injecting nitrogen, and there is a lack of an inert gas replenishment mechanism, resulting in a decrease in the preventive ability against goaf accidents.

[0008] To achieve the above object, a first aspect of the present invention provides a safety monitoring system for a goaf, including: a data acquisition module, an error calibration module and a model construction module;

[0009] Data acquisition module: Obtain the number of sensors in the goaf and the installation time; classify the sensors to obtain the sensor types; collect the environmental data in the goaf through the sensors; wherein, the environmental data includes: pressure and nitrogen concentration;

[0010] Error calibration module: Construct a reference value of the environmental data and determine an initial calibration value based on the reference value, calibrate the environmental data through the initial calibration value to obtain the initially calibrated environmental data, construct a compensation factor and perform secondary calibration based on the compensation factor to obtain the finally calibrated environmental data;

[0011] Model construction module: Use the average value of the reference value of the environmental data and the finally calibrated environmental data as inputs to construct a dynamic balance model, obtain the real-time replenishment amount of nitrogen through the dynamic balance model; determine whether the pressure in the goaf is within a preset normal pressure range; if so, mark it as the completion of the monitoring process; if not, construct a degradation mechanism and issue a warning signal.

[0012] Preferably, the classifying the sensors to obtain the sensor types includes:

[0013] Classify the sensors according to a preset classification rule to obtain the sensor types; wherein, the classification rules include: classification rule one, classification rule two and classification rule three;

[0014] The classification rule one is: Determine whether a sensor is replaced due to a failure in the goaf; if so, mark the replaced sensor as an A1 type sensor, and at the same time mark the sensors other than the A1 type sensors as B type sensors; if not, mark several sensors of the same batch as A1 type sensors;

[0015] The classification rule two is: Determine whether several sensors of the same batch are calibrated within a preset preventive maintenance period; if so, mark the calibrated sensors as A2 type sensors, and at the same time mark the sensors other than the A2 type sensors as B type sensors; if not, mark several sensors of the same batch as B type sensors;

[0016] The third classification rule is as follows: The A1 - type sensors that simultaneously meet the first classification rule and the A2 - type sensors that meet the second classification rule are marked as A - type sensors, and other sensor types except the A - type sensors are replaced and marked as B - type sensors.

[0017] Through the clear classification rules, the present invention can quickly classify sensors into different categories, facilitating subsequent management and maintenance; the first classification rule takes into account the sensors for fault replacement, ensuring that these sensors receive special attention and treatment, and avoiding potential risks brought by fault replacement; the second classification rule emphasizes the importance of preventive maintenance, and through the judgment of the calibration period, ensures the accuracy and reliability of the sensors; the third classification rule combines the first two rules, further refining the classification of sensors, making the maintenance strategy more precise and effective.

[0018] Preferably, constructing the reference value of the environmental data and determining the initial calibration value based on the reference value includes:

[0019] Retrieving the environmental data collected by the A - type and B - type sensors within the same set period, and setting the reference value K1 of the A - type sensors;

[0020] Denoting the absolute value of the difference between the reference value K1 and the environmental data collected by the B - type sensors as the error value Δx; where x represents the x - th sensor corresponding to the inconsistent environmental data collected by the B - type sensors with the reference value.

[0021] Judging whether the environmental data of the same type collected by i adjacent A - type sensors are all consistent; if yes, setting the initial calibration value J as half of the average error value; if not, sorting the environmental data S of the same type collected by i adjacent A - type sensors i in descending order; calculating the initial calibration value J through the formula where i is the number of sensors, and the value range of i is an integer greater than 2;

[0022] The method for obtaining the reference value of the A - type sensors is as follows: Randomly select i adjacent A - type sensors, and take the average of the environmental data collected by the corresponding i sensors as the reference value.

[0023] It should be noted that one environmental data is collected by the same sensor within the set period; the average error value is the average of all non - zero error values.

[0024] The present invention measures the deviation of the B - type sensors from the A - type reference through the error value, providing a clear numerical basis for calibration, facilitating subsequent adjustment or fault diagnosis; switching the calibration mode according to the consistency state of the data of adjacent sensors, thereby realizing fast adaptive calibration.

[0025] Preferably, the calibration of the environmental data by the initial calibration value to obtain the initially calibrated environmental data includes:

[0026] Retrieve the initial calibration value and the environmental data Bx collected by the type-B sensor, and record the absolute value of the difference between Bx - J and the reference value K1 as C1; record the absolute value of the difference between Bx + J and the reference value K1 as C2;

[0027] Determine whether C1 is greater than C2; if yes, mark Bx + J as the initially calibrated environmental data of the corresponding sensor; if not, mark Bx - J as the initially calibrated environmental data of the corresponding sensor.

[0028] Through comparison with the reference value K1, the present invention can evaluate the accuracy of the environmental data collected by the type-B sensor; according to the comparison results of C1 and C2, selecting the data closer to the reference value as the initially calibrated environmental data helps to improve the data accuracy.

[0029] Preferably, the construction of the compensation factor and the secondary calibration based on the compensation factor to obtain the finally calibrated environmental data includes:

[0030] Retrieve the initially calibrated environmental data;

[0031] Through the formula calculate the vibration compensation factor; where a x 、a y and a z are the instantaneous vibration accelerations in the X, Y, and Z axes respectively; r x 、r y and r z are the preset vibration sensitivity coefficients in the X, Y, and Z axes respectively;

[0032] Multiply the sub-data in the initially calibrated collected environmental data by (1 + Zb) respectively to obtain the corresponding sum product values, record the absolute value of the difference between the sum product value corresponding to the sub-data and the reference value of the sub-data in the environmental data as X1, multiply the sub-data in the initially calibrated collected environmental data by (1 - Zb) respectively to obtain the difference product values, and record the difference between the difference product value corresponding to the sub-data and the reference value of the sub-data in the environmental data as X2; determine whether X1 is greater than X2; if yes, mark the difference product value as the secondarily calibrated environmental data; if not, mark the sum product value as the secondarily calibrated environmental data;

[0033] Calculate the first difference between the initially calibrated environmental data and the reference value of the corresponding environmental data, calculate the second difference between the secondarily calibrated environmental data and the reference value of the corresponding environmental data, and select the environmental data corresponding to the smallest difference between the two differences as the finally calibrated environmental data.

[0034] It should be noted that the acquisition method of the preset vibration sensitivity coefficient is that the staff confirm the influence degree of each axial vibration on the sensor in the laboratory environment, construct a vibration sensitivity coefficient table according to the influence degree, and obtain the vibration sensitivity coefficients of each axis according to the vibration sensitivity coefficient table.

[0035] In the present invention, the vibration sensitivity coefficients of each axis are pre-calibrated in the laboratory to quantify the specific influence of vibrations in different directions on the sensor, avoid one-size-fits-all compensation, and achieve precise correction of anisotropic vibration errors; combining the instantaneous accelerations of the X, Y, and Z axes collected in real time, calculate the vibration compensation factor, so that the calibration parameters are dynamically adjusted according to the vibration intensity and direction, effectively coping with non-steady vibration scenarios such as vehicle bumps and equipment start-stop; by comparing the results of the two compensation directions of the sum product value and the difference product value, automatically select the calibration data closer to the reference value, avoiding overcorrection or undercorrection problems that may be introduced by a single compensation direction.

[0036] Preferably, obtaining the real-time replenishment amount of nitrogen through the dynamic balance model includes:

[0037] Retrieve the reference value of the nitrogen concentration data in the environmental data and the average value of the nitrogen concentration data in the secondarily calibrated environmental data as the real-time nitrogen concentration value;

[0038] Judge whether the difference ΔC between the real-time nitrogen concentration value and the preset nitrogen concentration safety threshold is greater than 0; if yes, mark it as stop filling nitrogen; if no, mark it as start filling nitrogen;

[0039] Construct the dynamic balance model as Calculate the real-time replenishment amount of nitrogen; where V is the effective volume in the preset goaf, s is the preset consumption rate of nitrogen reacting with minerals, φ is the preset nitrogen leakage rate, C is the real-time nitrogen concentration value, t v is the time of continuously filling nitrogen, is the nitrogen density under standard conditions.

[0040] In the present invention, when the nitrogen concentration exceeds the safety threshold, the system automatically marks it as stop filling nitrogen, ensuring that the nitrogen concentration in the goaf remains within the safe range; by constructing a dynamic balance model, the real-time replenishment amount of nitrogen can be accurately calculated, avoiding overfilling or underfilling of nitrogen.

[0041] Preferably, the acquisition method of the time of continuously filling nitrogen includes:

[0042] Retrieve the difference ΔC between the real-time nitrogen concentration value and the nitrogen concentration safety threshold;

[0043] Through the expression Calculate the nitrogen injection rate; where Q max is the maximum flow rate of the preset nitrogen injection equipment;

[0044] The time for continuously charging nitrogen is calculated by the formula , where LG is a dimensional coefficient.

[0045] Preferably, the construction of the feedback mechanism includes:

[0046] The nitrogen injection rate corresponding to ΔC > 5 ppm in the gob area is marked as level three, the nitrogen injection rate corresponding to 0 < ΔC ≤ 5 ppm is marked as level two, and the nitrogen injection rate corresponding to ΔC ≤ 0 is marked as level one; the nitrogen injection rate is replaced according to the downgrading mechanism; wherein, the downgrading mechanism is: the nitrogen injection rate corresponding to the original level three is downgraded to the nitrogen injection rate corresponding to level two, and the nitrogen injection rate corresponding to the original level two is downgraded to the nitrogen injection rate corresponding to level one.

[0047] Through hierarchical management, the present invention is beneficial to flexibly adjust the injection rate according to the actual change of nitrogen concentration, avoiding unnecessary resource waste, and reducing the injection rate through the downgrading mechanism, which helps to prevent the continuous increase of nitrogen concentration and ensure the safety of the gob area.

[0048] To achieve the above object, the second aspect of the present invention provides a safety monitoring method for a gob area, including:

[0049] Obtain the number of sensors in the gob area and the installation time; classify the sensors to obtain the sensor types; collect the environmental data in the gob area through the sensors; wherein, the environmental data includes: pressure and nitrogen concentration;

[0050] Construct a reference value of the environmental data and determine an initial calibration value based on the reference value, calibrate the environmental data through the initial calibration value to obtain the initially calibrated environmental data, construct a compensation factor and perform secondary calibration based on the compensation factor to obtain the finally calibrated environmental data;

[0051] Use the average value of the reference value of the environmental data and the finally calibrated environmental data as the input to construct a dynamic balance model, and obtain the real-time supplement amount of nitrogen through the dynamic balance model; judge whether the pressure in the gob area is within the preset normal pressure range; if so, mark it as the completion of the monitoring process; if not, construct a downgrading mechanism and issue a warning signal.

[0052] To achieve the above object, the third aspect of the present invention provides a safety monitoring computing device for a gob area, which is characterized in that it includes: a memory and a processor, and the memory stores executable instructions of the processor; wherein, the processor is configured to execute a safety monitoring system provided in the first aspect by executing the executable instructions.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] 1. Through the data acquisition module, the present invention can accurately obtain the number, installation time, and type of sensors in the goaf. The sensors can collect environmental data in real time, including pressure and nitrogen concentration, providing a basis for subsequent calibration and model construction; the error calibration module initially calibrates the environmental data by constructing a reference value and determining the initial calibration value, reducing the error of the original data; further, by constructing a compensation factor for secondary calibration, the accuracy of the data can be further improved, ensuring the reliability of the basic data for subsequent analysis and decision-making; the dynamic balance model is constructed by the model construction module using the reference value and the average value of the environmentally calibrated data after secondary calibration, which can predict the nitrogen supplement amount in real time, which is crucial for maintaining the environmental stability of the goaf.

[0055] 2. By judging whether the pressure is within the preset normal range, the system of the present invention can automatically trigger the warning mechanism, respond to possible abnormal situations in a timely manner, and reduce safety risks; through the degradation mechanism, when the pressure exceeds the normal range, the system can automatically construct a degradation mechanism and issue a warning signal, which helps to take measures in a timely manner to prevent the situation from deteriorating; at the same time, the intelligent warning system can reduce manual intervention, improve the response speed and accuracy, and reduce potential losses caused by environmental anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 It is a schematic diagram of the module relationship included in the present invention;

[0058] Figure 2 It is a schematic diagram of the specific steps of error calibration of the present invention;

[0059] Figure 3 It is a schematic diagram of the specific steps of model construction of the present invention;

[0060] Figure 4 It is a schematic diagram of the safety detection process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0061] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0062] Please refer to Figure 1 , an embodiment of the first aspect of the present invention provides a safety monitoring system for a goaf, including: a data acquisition module, an error calibration module, and a model construction module;

[0063] Data acquisition module: Obtain the number of sensors in the goaf and the installation time; classify the sensors to obtain the sensor types; collect the environmental data in the goaf through the sensors; wherein, the environmental data includes: pressure and nitrogen concentration;

[0064] Error calibration module: Construct a reference value of the environmental data and determine an initial calibration value based on the reference value, calibrate the environmental data through the initial calibration value to obtain the initially calibrated environmental data, construct a compensation factor and perform secondary calibration based on the compensation factor to obtain the finally calibrated environmental data;

[0065] Model construction module: Use the average value of the reference value of the environmental data and the finally calibrated environmental data as inputs to construct a dynamic balance model, and obtain the real-time nitrogen replenishment amount through the dynamic balance model; judge whether the pressure in the goaf is within the preset normal pressure range; if so, mark it as the monitoring process is completed; if not, construct a degradation mechanism and issue a warning signal.

[0066] Please refer to Figure 2 , the specific steps of error calibration: Classify the sensors according to the preset classification rules to obtain the sensor types; wherein, the classification rules include: classification rule one, classification rule two, and classification rule three;

[0067] The classification rule one is: Judge whether a sensor is replaced due to a failure in the goaf; if so, mark the replaced sensor as an A1 type sensor, and at the same time mark the sensors other than the A1 type sensors as B type sensors; if not, mark several sensors of the same batch as A1 type sensors;

[0068] The classification rule two is: Judge whether several sensors of the same batch are calibrated within the preset preventive maintenance period; if so, mark the calibrated sensors as A2 type sensors, and at the same time mark the sensors other than the A2 type sensors as B type sensors; if not, mark several sensors of the same batch as B type sensors;

[0069] The third classification rule is: Mark the A1 type sensors that meet the first classification rule and the A2 type sensors that meet the second classification rule as A type sensors, and replace and mark other sensor types except A type sensors as B type sensors.

[0070] Retrieve the environmental data collected by the A and B type sensors within the same set period, and set the reference value K1 of the A type sensors;

[0071] Record the absolute value of the difference between the reference value K1 and the environmental data of the same environment collected by the B type sensors as the error value Δx; where x represents the x-th sensor corresponding to the inconsistent environmental data collected by the B type sensors and the reference value.

[0072] Judge whether the environmental data of the same type collected by i adjacent A type sensors are all consistent; if yes, record the initial calibration value J as half of the average error value; if not, sort the environmental data S of the same type collected by i adjacent A type sensors i in descending order; calculate the initial calibration value J through the formula where i is the number of sensors, and the value range of i is an integer greater than 2;

[0073] Retrieve the initial calibration value and the environmental data Bx collected by the B type sensors, and record the absolute value of the difference between Bx - J and the reference value K1 as C1; record the absolute value of the difference between Bx + J and the reference value K1 as C2;

[0074] Judge whether C1 is greater than C2; if yes, mark Bx + J as the environmental data after the initial calibration of the corresponding sensor; if not, mark Bx - J as the environmental data after the initial calibration of the corresponding sensor;

[0075] Retrieve the environmental data after the initial calibration; calculate the vibration compensation factor Zb through the formula where a x 、a y and a z are the instantaneous vibration accelerations in the X, Y, and Z axes respectively; r x 、r y and r z are the preset vibration sensitivity coefficients in the X, Y, and Z axes respectively;

[0076] Multiply the sub - data in the environmental data collected after the initial calibration by (1 + Zb) respectively to obtain the corresponding sum - product values. Denote the absolute value of the difference between the sum - product value corresponding to the sub - data and the reference value corresponding to the sub - data in the environmental data as X1. Multiply the sub - data in the environmental data collected after the initial calibration by (1 - Zb) respectively to obtain the difference - product values. Denote the difference between the difference - product value corresponding to the sub - data and the reference value corresponding to the sub - data in the environmental data as X2. Judge whether X1 is greater than X2. If so, mark the difference - product value as the environmental data after the secondary calibration. If not, mark the sum - product value as the environmental data after the secondary calibration.

[0077] Calculate the first - order difference between the environmental data after the initial calibration and the reference value of the corresponding environmental data, calculate the second - order difference between the environmental data after the secondary calibration and the reference value of the corresponding environmental data, and select the environmental data corresponding to the minimum difference between the two differences as the environmental data after the final calibration.

[0078] For example, there are 10 sensors in the goaf of a mine. The preset preventive maintenance cycle is one year. Among them, the installation dates of sensors C1 - C3 are 2021.03.23. Sensors C4 and C5 are replacement sensors. And C1 - C5 are all calibrated within one year of the corresponding maintenance cycle. The installation date of C4 is 2022.01.06, and the installation date of C5 is 2022.09.11. The installation dates of sensors C6 - C10 are all 2021.06.16. And C6 - C9 are calibrated within one year of the maintenance cycle, while C10 is not calibrated within one year of the maintenance cycle.

[0079] Preset classification rules; among them, the classification rules include: Classification Rule One, Classification Rule Two, and Classification Rule Three.

[0080] Classification Rule One is: Judge whether a sensor is replaced due to a failure in the goaf. If so, mark the replaced sensor as an A1 - type sensor, and at the same time mark the remaining sensors except the replaced one as B - type sensors. If not, mark several sensors of the same batch as A1 - type sensors.

[0081] Classification Rule Two is: Judge whether several sensors of the same batch are calibrated within the preset preventive maintenance cycle. If so, mark the corresponding sensors as A2 - type sensors, and at the same time mark the remaining sensors except those that have been calibrated as B - type sensors. If not, mark several sensors of the same batch as B - type sensors.

[0082] Classification Rule Three is: Mark the A1 - type sensors that meet Classification Rule One and the A2 - type sensors that meet Classification Rule Two as A - type sensors, and replace and mark the other sensors except the A - type sensors as B - type sensors.

[0083] Based on the preset classification rules, the sensors G4 - G9 are marked as type A sensors, and the sensors G1 - G3, G10 are marked as type B sensors;

[0084] Retrieve the nitrogen concentration data and pressure data collected by both type A and type B sensors within the same week. Set the nitrogen concentration S i collected by the type A sensors G4 - G9 to be 36 ppm each, then the reference value K1 = 36 ppm; where i = 1, 2, 3, …, 6;

[0085] The nitrogen concentration collected by the type B sensor G1 is B1 = 35.6 ppm, by G2 is B2 = 36.2 ppm, by G3 is 36 ppm, and by G10 is 36 ppm; Since the nitrogen concentrations collected by G3 and G10 are the same as the reference value, there is no need to calibrate G3 and G10;

[0086] Denote the absolute value of the difference between the reference value K1 and the nitrogen concentration collected by the type B sensor G1 as the error value Δx = 0.4 ppm; Denote the absolute value of the difference between the reference value K1 and the nitrogen concentration collected by the type B sensor G2 as the error value Δ2 = 0.2 ppm;

[0087] The nitrogen concentration data collected by the type A sensors G4 - G9 are all the same, then the initial calibration value J = (0.2 + 0.1) / 2 = 0.15 ppm;

[0088] The absolute value of the difference between B1 - J and the reference value K1 is C1 = |35.6 ppm - 0.25 ppm - 36 ppm| = 0.65 ppm; The absolute value of the difference between B1 + J and the reference value K1 is C2 = |35.6 ppm + 0.25 ppm - 36 ppm| = 0.15 ppm; Since C1 > C2, the nitrogen concentration collected by the sensor G1 is 35.85 ppm;

[0089] The absolute value of the difference between B2 - J and the reference value K1 is C1 = |36.2 ppm - 0.15 ppm - 36 ppm| = 0.05 ppm; The absolute value of the difference between B2 + J and the reference value K1 is C2 = |36.2 ppm + 0.15 ppm - 36 ppm| = 0.35 ppm; Since C1 < C2, the nitrogen concentration collected by the sensor G2 is 35.95 ppm;

[0090] Judge whether there is a situation where the instantaneous vibration acceleration in each axis is greater than 3 m / s 2 ;

[0091] If so, mark the influence degree of the vibration in the corresponding axis on the sensor as level one;

[0092] If not, it is determined whether there is a case where the instantaneous acceleration of vibration in each axial direction is less than 0.5 m / s 2 ; if so, the influence degree of the vibration in the corresponding axial direction on the sensor is marked as level three; if not, the influence degree of the vibration in the corresponding axial direction on the sensor is marked as level two;

[0093] Table 1 Vibration Sensitivity Coefficient Table

[0094]

[0095]

[0096] The value of a x = 2.5 m / s 2 、a y = 0.46 m / s 2 ,a z = 1.72 m / s 2 ; then the influence degree of the vibration in the X axial direction on the sensor is marked as level two, the influence degree of the vibration in the Y axial direction on the sensor is marked as level three, and the influence degree of the vibration in the Z axial direction on the sensor is marked as level two;

[0097] According to the vibration sensitivity coefficient table (as shown in Table 1), r x = 0.75, r y = 0.49, and r z = 0.62;

[0098] Through the formula Calculate the vibration compensation factor Zb;

[0099] Retrieve the nitrogen concentration of the Class B sensor after the initial calibration as 35.95 ppm;

[0100] Record the absolute value of the difference between 35.95×(1 + 0.023) and the reference value of 36 ppm as X1 = 0.77, and record the difference between YS(1 - Zb) and the reference value as X2 = 0.876; since X1 is less than X2; then mark 36.77 as the environmental data after the secondary calibration;

[0101] Calculate the first difference between the environmental data after the initial calibration and the nitrogen concentration reference value as 0.05, calculate the second difference between the environmental data after the secondary calibration and the nitrogen concentration reference value as 0.77, since 0.77 is greater than 0.05, select the smaller difference of the two differences, and select the environmental data 35.95 ppm corresponding to 0.05 as the nitrogen concentration data after the final calibration;

[0102] According to the above steps, the pressure value after the final calibration can be obtained.

[0103] Please refer to Figure 3, Specific steps for model construction: Retrieve the benchmark value of the nitrogen concentration data in the environmental data and the average value of the nitrogen concentration data in the environmentally data after secondary calibration as the real-time nitrogen concentration value;

[0104] Determine whether the difference ΔC between the real-time nitrogen concentration value and the preset nitrogen concentration safety threshold is greater than 0; if so, mark it as stop nitrogen injection; if not, mark it as start nitrogen injection;

[0105] Construct a dynamic equilibrium model as Calculate the real-time replenishment amount of nitrogen; in the formula, V is the effective volume in the preset goaf, s is the preset consumption rate of nitrogen reacting with minerals, φ is the preset nitrogen leakage rate, C is the real-time nitrogen concentration value; t v is the time for continuous nitrogen injection, is the nitrogen density under standard conditions;

[0106] Retrieve the difference ΔC between the real-time nitrogen concentration value and the nitrogen concentration safety threshold;

[0107] Through the expression Calculate the nitrogen injection rate; where, Q max is the maximum flow rate of the preset nitrogen injection equipment;

[0108] Through the formula Calculate the time for continuous nitrogen injection; where, LG is the dimension coefficient;

[0109] Label the nitrogen injection rate corresponding to ΔC > 5 ppm in the goaf as level three, label the nitrogen injection rate corresponding to 0 < ΔC ≤ 5 ppm as level two, and label the nitrogen injection rate corresponding to ΔC ≤ 0 as level one; replace the nitrogen injection rate according to the downgrading mechanism; where, the downgrading mechanism is: the nitrogen injection rate corresponding to the original level three is downgraded to the nitrogen injection rate corresponding to level two, and the nitrogen injection rate corresponding to the original level two is downgraded to the nitrogen injection rate corresponding to level one.

[0110] For example, retrieve the average value of the benchmark value 36 ppm of the nitrogen concentration data in the environmental data and 35.95 ppm of the nitrogen concentration data in the environmentally data after secondary calibration as the real-time nitrogen concentration value 35.975 ppm;

[0111] The preset nitrogen concentration safety threshold is 37.2 ppm. Since the difference ΔC between the real-time nitrogen concentration value and the nitrogen concentration safety threshold is ΔC = 35.975 ppm - 37.2 ppm = -1.225 ppm < 0, mark it as start nitrogen injection;

[0112] The effective volume V in the goaf is 156 m 3 , the preset consumption rate s of nitrogen reacting with minerals is 0.32 mol·L -1·s -1 The preset nitrogen leakage rate φ is 0.51 Pa·m 3 / s, C is the real-time nitrogen concentration value of 35.975 ppm; the nitrogen density under standard conditions is 1.25 kg / m 3 ; Q max is the maximum flow rate Q of the preset nitrogen injection equipment max which is 6.8 m 3 / s;

[0113] Since ΔC = -1.225 ppm ≤ 0, then

[0114] The time for continuously injecting nitrogen is calculated through the formula where the dimension coefficient LG = 1 Pa·m 6 ·ppm·kg -1 ;

[0115] A dynamic equilibrium model is constructed as The calculated real-time nitrogen supplement amount is 34.9 ppm.

[0116] Please refer to Figure 4 , the second aspect embodiment of the present invention provides a safety monitoring method for a goaf, including:

[0117] Obtain the number of sensors in the goaf and the installation time; classify the sensors to obtain the sensor types; collect the environmental data in the goaf through the sensors; where the environmental data includes: pressure and nitrogen concentration;

[0118] Construct a reference value of the environmental data and determine an initial calibration value based on the reference value, calibrate the environmental data through the initial calibration value to obtain the initially calibrated environmental data, construct a compensation factor and perform secondary calibration based on the compensation factor to obtain the finally calibrated environmental data;

[0119] Use the average value of the reference value of the environmental data and the finally calibrated environmental data as the input to construct a dynamic equilibrium model, obtain the real-time nitrogen supplement amount through the dynamic equilibrium model; determine whether the pressure in the goaf is within the preset normal pressure range; if yes, mark it as the completion of the monitoring process; if not, construct a degradation mechanism and issue a warning signal.

[0120] The third aspect embodiment of the present invention provides a safety monitoring calculation device for a goaf, including: a memory and a processor, where the memory stores executable instructions configured by the processor; where the processor is configured to execute a safety monitoring system for a goaf provided in the first aspect via executing the executable instructions.

[0121] Some of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is obtained by software simulation of a large amount of collected data to get a formula that is closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0122] The working principle of the present invention: The present invention obtains the number of sensors in the goaf and the installation time; classifies the sensors to obtain the sensor types; collects the environmental data in the goaf through the sensors; constructs the reference value of the environmental data and determines the initial calibration value based on the reference value, calibrates the environmental data through the initial calibration value to obtain the initially calibrated environmental data, constructs the compensation factor and performs secondary calibration based on the compensation factor to obtain the finally calibrated environmental data; takes the average value of the reference value of the environmental data and the finally calibrated environmental data as the input to construct a dynamic balance model, and obtains the real-time supplement amount of nitrogen through the dynamic balance model; determines whether the pressure in the goaf is within the preset normal pressure range; if so, marks the completion of the monitoring process; if not, constructs a degradation mechanism and issues a warning signal.

[0123] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A safety monitoring system for goaf areas, characterized in that: include: Data acquisition module, error calibration module and model building module; Data acquisition module: obtain the number of sensors in the goaf and the time of installation; Classify the sensors to obtain sensor types; Collect environmental data in the goaf through sensors; the environmental data includes: pressure and nitrogen concentration; Error calibration module: constructs a baseline value of environmental data and determines an initial calibration value based on the baseline value, calibrates the environmental data using the initial calibration value to obtain the environmental data after the initial calibration, constructs a compensation factor and performs a secondary calibration based on the compensation factor to obtain the environmental data after the final calibration; Model building module: The baseline value of environmental data and the average value of the final calibrated environmental data are used as input to construct a dynamic balance model, and the real-time replenishment amount of nitrogen is obtained through the dynamic balance model; determine whether the pressure of the goaf is within the preset normal pressure range; if yes, mark the monitoring process as completed; if not, build a degradation mechanism and issue an early warning signal.

2. A safety monitoring system for goaf areas according to claim 1, characterized in that: The method of classifying the sensor to obtain the sensor type is: classifying according to a preset classification rule; wherein the classification rule includes: classification rule one, classification rule two, and classification rule three; The first classification rule is: determine whether the sensor in the goaf is replaced due to a fault; if yes, mark the replaced sensor as a Class A1 sensor, and mark the sensors other than Class A1 sensors as Class B sensors; if no, mark several sensors in the same batch as Class A1 sensors; The second classification rule is: determine whether several sensors of the same batch have completed calibration within a preset preventive maintenance cycle; if yes, mark the sensors that have completed calibration as Class A2 sensors, and mark the sensors other than Class A2 sensors as Class B sensors; if no, mark several sensors of the same batch as Class B sensors; The classification rule three is: class A1 sensors that meet both the classification rule one and class A2 sensors that meet the classification rule two are marked as class A sensors, and sensor types other than class A sensors are replaced and marked as class B sensors.

3. A safety monitoring system for goaf areas according to claim 1, characterized in that: The step of constructing a reference value of the environmental data and determining an initial calibration value based on the reference value comprises: Retrieve the environmental data collected by sensors A and B in the same set period, and set the reference value K1 of sensor A; The absolute value of the difference between the reference value K1 and the same environmental data collected by the type B sensor is recorded as the error value Δx; where x is the xth sensor corresponding to the time when the environmental data collected by the type B sensor is inconsistent with the reference value; Determine whether the same type of environmental data collected by i adjacent type A sensors are consistent; if yes, record the initial calibration value J as half of the average error value; if no, record the same type of environmental data S collected by i adjacent type A sensors as i Arrange in descending order; obtain the initial calibration value J by calculating the average value of the difference between adjacent error values; wherein, i is the number of sensors, and the value range of i is an integer greater than 2.

4. A safety monitoring system for goaf areas according to claim 1, characterized in that: The step of calibrating the environmental data by using the initial calibration value to obtain the initially calibrated environmental data includes: Retrieve the initial calibration value and the environmental data Bx collected by the type B sensor, record the absolute value of the difference between Bx-J and the reference value K1 as C1; record the absolute value of the difference between Bx+J and the reference value K1 as C2; Determine whether C1 is greater than C2; if yes, mark Bx+J as the environmental data after the initial calibration of the corresponding sensor; if no, mark Bx-J as the environmental data after the initial calibration of the corresponding sensor.

5. A safety monitoring system for goaf areas according to claim 1, characterized in that: The method of constructing a compensation factor and performing secondary calibration based on the compensation factor to obtain final calibrated environmental data includes: Retrieve environmental data after initial calibration; The vibration compensation factor Zb is calculated by the mapping relationship between the instantaneous acceleration of vibration and the preset vibration sensitivity coefficient; The sub-data in the environmental data collected after the initial calibration are respectively multiplied by (1+Zb) to obtain the corresponding sum product value, and the absolute value of the difference between the sum product value corresponding to the sub-data and the reference value corresponding to the sub-data in the environmental data is recorded as X1; the sub-data in the environmental data collected after the initial calibration are respectively multiplied by (1-Zb) to obtain the difference product value, and the difference between the difference product value corresponding to the sub-data and the reference value corresponding to the sub-data in the environmental data is recorded as X2; determine whether X1 is greater than X2; if yes, mark the difference product value as the environmental data after the secondary calibration; if not, mark the sum product value as the environmental data after the secondary calibration; Calculate the first difference between the environmental data after the initial calibration and the reference value of the corresponding environmental data, calculate the second difference between the environmental data after the secondary calibration and the reference value of the corresponding environmental data, and select the environmental data corresponding to the smallest difference between the two differences as the final calibrated environmental data.

6. A safety monitoring system for goaf areas according to claim 1, characterized in that: The real-time supplement amount of nitrogen obtained by the dynamic balance model includes: The baseline value of nitrogen concentration data in the environmental data and the average value of nitrogen concentration data in the environmental data after secondary calibration are retrieved as the real-time nitrogen concentration value; Determine whether the difference ΔC between the real-time nitrogen concentration value and the preset nitrogen concentration safety threshold is greater than 0; if yes, mark as stop nitrogen filling; if no, mark as start nitrogen filling; The dynamic equilibrium model is constructed as The real-time nitrogen replenishment amount is calculated; where V is the preset effective volume in the goaf, s is the preset consumption rate of nitrogen and mineral reaction, φ is the preset nitrogen leakage rate, C is the real-time nitrogen concentration value, t v The time for continuous nitrogen filling. is the density of nitrogen under standard conditions.

7. A safety monitoring system for goaf areas according to claim 6, characterized in that: The method for obtaining the continuous nitrogen filling time includes: Retrieve the difference ΔC between the real-time nitrogen concentration value and the nitrogen concentration safety threshold; By expressing Calculate the nitrogen injection rate; where Q max The maximum flow rate of the preset nitrogen injection equipment; By formula The continuous nitrogen filling time is calculated; where LG is the dimension coefficient.

8. A safety monitoring system for goaf areas according to claim 1, characterized in that: The feedback mechanism is constructed, including: The nitrogen injection rate corresponding to ΔC>5ppm in the goaf is marked as level three, the nitrogen injection rate corresponding to 0<ΔC≤5ppm is marked as level two, and the nitrogen injection rate corresponding to ΔC≤0 is marked as level one; the nitrogen injection rate is replaced according to the degradation mechanism; wherein, the degradation mechanism is: the nitrogen injection rate corresponding to the original level three is downgraded to the nitrogen injection rate corresponding to the level two, and the nitrogen injection rate corresponding to the original level two is downgraded to the nitrogen injection rate corresponding to the level one.

9. A safety monitoring method for goaf area, adapted to a safety monitoring system for goaf area according to any one of claims 1 to 8, characterized in that: include: Get the number of sensors in the goaf and the time of installation; Classify the sensors to obtain sensor types; Collect environmental data in the goaf through sensors; the environmental data includes: pressure and nitrogen concentration; Constructing a reference value of environmental data and determining an initial calibration value based on the reference value, calibrating the environmental data by the initial calibration value to obtain the environmental data after the initial calibration, constructing a compensation factor and performing a secondary calibration based on the compensation factor to obtain the environmental data after the final calibration; The baseline value of the environmental data and the average value of the final calibrated environmental data are used as input to construct a dynamic balance model, and the real-time nitrogen replenishment amount is obtained through the dynamic balance model; it is determined whether the pressure of the goaf is within the preset normal pressure range; if yes, it is marked as the completion of the monitoring process; if not, a degradation mechanism is constructed and an early warning signal is issued.

10. A safety monitoring computing device for goaf areas, having a computer-readable storage medium stored thereon, characterized in that: When the computer-readable storage medium is executed by a processor, the computer-readable storage medium implements a safety monitoring method for a goaf area as described in any one of claims 1 to 8.

Images