A method for early warning of gas outburst based on quantitative characterization of continuous increase in gas outburst volume

Abstract

The present invention discloses a prominent early warning method based on the quantitative characterization of the continuous increase in gas emission volume. In the initial stage, a monitoring and transmission time is set, and the monitoring data of gas emission is monitored and transmitted once every monitoring and transmission time. Dynamically count the gas emission data within a certain unit time, and analyze the following three indicators: the total number of times the monitoring data within the unit time is greater than all the previous data of the monitoring data, the difference between the highest value and the lowest value of the monitoring data within the unit time, and the total number of times the monitoring data within the unit time is less than all the subsequent data of the monitoring data. The above three indicators are used to comprehensively characterize the phenomenon of the continuous increase in gas emission volume. When all these three indicators exceed the limit, an early warning is initiated.

Description

Technical Field

[0001] The present invention relates to a method for early warning of coal and gas outburst, and particularly to an outburst early warning method based on quantitative characterization of continuous increase in gas emission volume. Background Art

[0002] Coal and gas outburst is a dynamic disaster, seriously threatening the safety production of coal mines. Therefore, it is necessary to take preventive measures against coal and gas outburst disasters in advance.

[0003] Both the "Coal Mine Safety Regulations" and the "Rules for Prevention and Control of Coal and Gas Outburst" have given prediction indexes and methods for coal and gas outburst. Especially in Article 50 of the "Rules for Prevention and Control of Coal and Gas Outburst", it is considered that continuous increase in gas emission volume is a typical outburst omen.

[0004] At present, the early warning indexes based on continuous increase in gas emission volume mainly include the peak-valley ratio of gas emission volume, gas emission characteristic value, abnormal rate of gas emission volume, etc. These indexes mainly reflect the difference of gas emission volume before and after, but do not reflect the concept of "continuous". Therefore, it is urgent to explore new indexes that can quantitatively characterize the continuous increase in gas emission volume.

[0005] The present invention provides an outburst early warning method based on quantitative characterization of continuous increase in gas emission volume. Through the application of this method, the accuracy of coal and gas outburst early warning based on gas emission characteristics is improved. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to propose an outburst early warning method based on quantitative characterization of continuous increase in gas emission volume.

[0007] The technical solution adopted by the present invention is: an outburst early warning method based on quantitative characterization of continuous increase in gas emission volume. In the initial stage, the monitoring and transmission time is set, and the monitoring data of gas emission is monitored and transmitted once every monitoring and transmission time. Dynamically count the gas emission data within a certain unit time, and analyze the following three indexes: the total number of times that the monitoring data within the unit time is greater than all the previous data of the monitoring data, the difference between the highest value and the lowest value of the monitoring data within the unit time, and the total number of times that the monitoring data within the unit time is less than all the later data of the monitoring data. The continuous increase phenomenon of gas emission volume is comprehensively characterized by the above three indexes. When all these three indexes exceed the limit, the early warning is started. The specific steps are as follows:

[0008] Step 1) Use the gas monitoring system to monitor the gas concentration and air volume, and calculate the gas emission volume through the gas concentration and air volume, which is the gas emission data.

[0009] Step 2) Set the unit time N for gas emission data monitoring, the total number of monitoring times i within the unit time, and the gas monitoring transmission time n; the gas emission data obtained during each monitoring transmission time n within the unit time N is the monitoring data.

[0010] Step 3) Determine the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data

[0011] 3.1) Sequentially determine whether each monitoring data within the unit time is greater than all the previous data of this monitoring data. If "yes", record it as "1"; if "no", record it as "0", and accumulate and calculate to determine the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data;

[0012] 3.2) Set the critical value m1 for the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data. The value range of the critical value m1 is 0.5i - 1.0i;

[0013] 3.3) Compare the total number of times L1 with the total number of times critical value m1 to determine whether L1 reaches or exceeds the critical value.

[0014] The specific calculation method is: the quantitative characterization of the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data

[0015] When x j >max|x1,......,x j-1 |, g(x j ) = 1; otherwise, g(x j ) = 0;

[0016] x j is the jth monitoring number, j ≥ 2; g is the cumulative function of the increasing times of the highest gas emission value;

[0017]

[0018] In the above formula, i is the total number of monitoring times within the unit time, i ≥ j; L1 is the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data; m1 is the critical value of the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data.

[0019] Step 4) Determine the difference Δx between the highest value and the lowest value of the monitoring data within the unit time;

[0020] 4.1) Calculate the difference Δx between the highest value and the lowest value of the monitoring data within the unit time;

[0021] 4.2) Set the critical value S for the difference between the highest value and the lowest value of the monitoring data within the unit time. The range of the critical value S is 0.5% - 1% of the air volume;

[0022] 4.3) Compare the difference Δx between the highest value and the lowest value with the critical value S to determine whether Δx reaches or exceeds the critical value.

[0023] The specific calculation method is: the quantitative characterization of the difference Δx between the highest value and the lowest value of the monitoring data within a unit time

[0024] Δx = max|x1,......,x i | - min|x1,......,x i | ≥ S (2)

[0025] In the above formula, Δx is the difference between the highest value and the lowest value of the monitoring data within a unit time; S is the critical value of the difference between the highest value and the lowest value of the monitoring data within a unit time.

[0026] Step 5) Determine the total number L2 of times that the monitoring data within a unit time is less than all the subsequent data of this monitoring data

[0027] 5.1) Determine in sequence whether each monitoring data within a unit time is less than all the subsequent data of this monitoring data. If "yes", record it as "1"; if "no", record it as "0", and accumulate the calculation to determine the total number L2 of times that the monitoring data within a unit time is less than all the subsequent data of this monitoring data;

[0028] 5.2) Set the critical value m2 of the total number of times that the monitoring data is less than all the subsequent data of this monitoring data. The value range of the critical value m2 is 0.5i - 1.0i;

[0029] 5.3) Compare the total number L2 with the critical value m2 of the total number of times to determine whether L2 reaches or exceeds the critical value.

[0030] The specific calculation method is: the quantitative characterization of the total number L2 of times that the monitoring data within a unit time is less than all the subsequent data of this monitoring data

[0031] If x j <min|x j+1 ,......,x i |, then h(x j ) = 1; otherwise, h(x j ) = 0;

[0032] h is the cumulative function of the number of times the minimum outburst volume decreases;

[0033]

[0034] In the above formula, L2 is the total number of times that the monitoring data within a unit time is less than all the subsequent data of this monitoring data; m2 is the critical value of the total number of times that the monitoring data within a unit time is less than all the subsequent data of this monitoring data.

[0035] Step 6) Starting from the initial monitoring, when the monitoring transmission time n is less than or equal to the unit time N, if all three indicators L1, Δx, and L2 reach or exceed the critical values, that is, L1≥m1, Δx≥S, and L2≥m2, the early warning is directly initiated; if any one of the three indicators L1, Δx, and L2 does not reach the critical value, when the monitoring transmission time n is equal to the unit time N, the cyclic monitoring early warning is initiated, always ensuring that the quantity of data used for risk analysis, that is, the monitoring data within the most recent unit time, is fixed. For each newly added set of monitoring data, one set of the earliest monitoring data within the most recent unit time is deleted.

[0036] Step 7) After initiating the cyclic early warning, continue to statistically analyze the data and determine the over-limit conditions of the three indicators L1, Δx, and L2 to determine whether to initiate the early warning.

[0037] The above-mentioned monitoring data is dynamically statistically analyzed. Therefore, L1, Δx, and L2 within the unit time are not fixed and change with the change of the monitoring data.

[0038] The beneficial effects of the present invention are as follows: The continuous increase in the gas emission volume is a typical omen of coal and gas outburst. The early warning indicators (L1, Δx, L2) proposed by the present invention can accurately and quantitatively describe the phenomenon of the continuous increase in the gas emission volume, and can further improve the accuracy of the intelligent early warning of coal and gas outburst risks based on the gas emission characteristics. Description of the Drawings

[0039] Figure 1 is a schematic diagram of the gas emission statistical process within the unit time. Detailed Embodiment

[0040] A method for early warning of outburst based on quantitative characterization of continuous increase in gas emission volume of the present invention specifically comprises the following operating steps:

[0041] 1. Under the conditions of normal ventilation and normal tunneling in the working face, a gas monitoring system is established by using a sensor device, which is mainly responsible for monitoring the gas concentration and air volume, and the product of the two is the gas emission volume.

[0042] 2. Determine the length of the unit time N, that is, the single cycle time, such as 30 min, etc. Set a monitoring transmission time n with a fixed duration within the unit time N. Usually, data is monitored and transmitted once every 30 seconds, determine the total number of monitoring times i within the unit time N, and dynamically statistically analyze the gas emission volume data within the unit time N.

[0043] 3. Use computer software to process the gas emission data transmitted by the gas monitoring system in real time. The content includes three items: First, determine in sequence whether the monitoring data obtained at each monitoring transmission time n within the unit time N is greater than all the monitoring data in the early stage of this monitoring data within the unit time. If "yes", record it as "1"; if "no", record it as "0" and accumulate the calculation to determine the total number of times L1 that the monitoring data within the unit time N is greater than all the previous data of this monitoring data. Second, calculate the difference Δx between the maximum value and the minimum value of the monitoring data within the unit time N. Third, determine in sequence whether the monitoring data obtained at each monitoring transmission time n within the unit time N is less than all the monitoring data in the later stage of this monitoring data within the unit time N, and accumulate the calculation to determine the total number of times L2 that the monitoring data within the unit time N is less than all the later data of this monitoring data.

[0044] Set the critical values m1, m2 and S respectively. The value ranges of the critical values m1 and m2 are 0.5i - 1.0i, and the range of the critical value S is 0.5% - 1% of the air volume.

[0045] 4. If all the above three indicators reach or exceed the critical values, that is, L1≥m1, Δx≥S, L2≥m2, then start the early warning.

[0046] 5. Starting from the initial monitoring, when the monitoring transmission time n is less than or equal to the unit time N, if the three indicators meet the requirements, directly start the early warning; otherwise, if one of the three indicators does not meet the requirements, when the monitoring transmission time n is equal to the unit time N, start the cyclic monitoring early warning, always ensuring that the quantity of data used for risk analysis, that is, the monitoring data within the most recent unit time, is fixed. For each newly added set of monitoring data, one set of the earliest monitoring data within the most recent unit time should be deleted.

[0047] 6. After starting the cyclic early warning, continue to statistically analyze the data and determine the over-limit situation of the three indicators to determine whether to start the early warning.

[0048] Example 1

[0049] For easy understanding, taking the 60 groups of data monitored within 30 minutes as an example, whether to start the early warning is introduced as follows:

[0050] 1. The 60 groups of gas emissions monitored within 30 minutes are shown in Table 1.

[0051] Table 1 Gas Emission Monitoring Data Table

[0052]

[0053]

[0054] In Table 1, the monitoring sequence indicates the order of monitoring time. For example, "1" represents the monitoring at 8 o'clock, "2" represents the monitoring at 8:30, "3" represents the monitoring at 8:01, and so on.

[0055] 2. Calculate the total number L1 of times when the monitoring data within 30 minutes is greater than all previous data.

[0056] The gas emission amount monitored at "2" is 1.6, and the gas emission amount monitored at "1" is 1.5. Since 1.6 > 1.5, the cumulative function g(x2) = 1; the maximum gas emission amount monitored at "1" and "2" is 1.6, and the gas emission amount monitored at "3" is 1.4. Since 1.4 < 1.6, the cumulative function g(x3) = 0; the maximum gas emission amount monitored at "1", "2", and "3" is 1.6, and the gas emission amount monitored at "4" is 1.3. Since 1.3 < 1.6, the cumulative function g(x4) = 0; the maximum gas emission amount monitored at "1", "2", "3", and "4" is 1.6, and the gas emission amount monitored at "5" is 1.7. Since 1.7 > 1.6, the cumulative function g(x5) = 1; and so on, until calculating g(x 60 )

[0057] L1 = g(x2) + g(x3) + g(x4) + … + g(x 60 )

[0058] 3. Calculate the difference Δx between the highest value and the lowest value of the monitoring data within 30 minutes.

[0059] The gas emission amount monitored at "8" is 1.0, which belongs to the lowest value, and the gas emission amounts monitored at "53", "55", and "59" are all 2.9, which belong to the highest values. Then the difference Δx between the highest value and the lowest value is 1.9.

[0060] 4. Calculate the total number L2 of times when the monitoring data within 30 minutes is less than all subsequent data.

[0061] The gas emission amount monitored at "59" is 2.9, and the gas emission amount monitored at "60" is 2.8. Since 2.9 > 2.8, the cumulative function h(x 59 ) = 0; the minimum gas emission amount monitored at "59" and "60" is 2.8, and the gas emission amount monitored at "58" is 2.8. Since 2.8 = 2.8, the cumulative function h(x 58 ) = 0; the minimum gas emission amount monitored at "58", "59", and "60" is 2.8, and the gas emission amount monitored at "57" is 2.6. Since 2.6 < 2.8, the cumulative function h(x 57) = 1; The minimum gas emission volume monitored by "57", "58", "59" and "60" is 2.6, and the gas emission volume monitored by "56" is 2.1. Since 2.1 < 2.6, the cumulative function h(x 56 ) = 1; And so on, until h(x1) is calculated.

[0062] L2 = h(x1) + h(x2) + h(x3) + … + h(x 59 )

[0063] 5. Determine whether to start early warning.

[0064] If all of the above three indicators reach or exceed the critical values, start early warning, that is, L1 ≥ m1, Δx ≥ S, L2 ≥ m2.

[0065] It should be noted that:

[0066] 1. For the initial stage of monitoring, that is, when the monitoring time is less than 30 minutes and the number of monitoring data is less than 60 groups, directly use the monitored data for calculation and analysis. For example, at 8:05, only the first 10 groups of data are monitored, and the data of groups 1 - 10 are used for calculation and analysis.

[0067] 2. For the situation where it has exceeded 30 minutes, take the 60 groups of data monitored in the most recent 30 minutes for calculation and analysis. For example, at 8:31, take the data from 8:01 to 8:31 for calculation and analysis, that is, delete the data at 8:00 and 8:30 seconds, and add the data at 8:30 and 8:30 minutes and 30 seconds.

Claims

1. A prominent early warning method based on the quantitative characterization of the continuous increase in gas emission volume, characterized in that, It includes the following steps: Step 1) Use the gas monitoring system to monitor the gas concentration and air volume, and calculate the gas emission volume through the gas concentration and air volume, which is the gas emission data; Step 2) Set the unit time N for gas emission data monitoring, the total number of monitoring times i within the unit time, and the gas monitoring transmission time n; the gas emission data obtained at each monitoring transmission time n within the unit time N is the monitoring data; Step 3) Judge whether the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data Sequentially judge whether each monitoring data within the unit time is greater than all the previous data of this monitoring data. If "yes", record it as "1", if "no", record it as "0", and accumulate and calculate to determine the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data; Set the critical value m1 of the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data; Compare the total number of times L1 with the critical value m1 to judge whether L1 reaches or exceeds the critical value; Step 4) Judge the difference Δx between the highest value and the lowest value of the monitoring data within the unit time; Calculate the difference Δx between the highest value and the lowest value of the monitoring data within the unit time; Set the critical value S of the difference between the highest value and the lowest value of the monitoring data within the unit time; Compare the difference Δx between the highest value and the lowest value with the critical value S to judge whether Δx reaches or exceeds the critical value; Step 5) Judge the total number of times L2 that the monitoring data within the unit time is less than all the subsequent data of this monitoring data Sequentially judge whether each monitoring data within the unit time is less than all the subsequent data of this monitoring data. If "yes", record it as "1", if "no", record it as "0", and accumulate and calculate to determine the total number of times L2 that the monitoring data within the unit time is less than all the subsequent data of this monitoring data; Set the critical value m2 of the total number of times that the monitoring data is less than all the subsequent data of this monitoring data; Compare the total number of times L2 with the critical value m2 to judge whether L2 reaches or exceeds the critical value; Step 6) Starting from the initial monitoring, when the monitoring transmission time n is less than the unit time N, if all three indicators L1, Δx, and L2 reach or exceed the critical values, that is, L1≥m1, Δx≥S, L2≥m2, then directly start the early warning.

2. The outburst early warning method based on quantitative characterization of continuous increase in gas emission amount according to claim 1, characterized in that The value range of the critical values m1 and m2 is 0.5i - 1.0i, and the range of the critical value S is 0.5% - 1% of the air volume.

3. The outburst early warning method based on quantitative characterization of continuous increase in gas emission amount according to claim 1, wherein In step 3), the quantitative characterization of the total number of times L1 that the monitoring data within the unit time is greater than all the previous data of this monitoring data When x j > max|x1,......,x j-1 |, g(x j ) = 1; otherwise, g(x j ) = 0; x j is the number of the j-th monitoring, where j ≥ 2; g is the cumulative function of the increasing times of the maximum outburst volume; In the above formula, i is the total number of monitoring times within the unit time, i≥j; L1 is the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data; m1 is the critical value of the total number of times that the monitoring data within the unit time is greater than all the previous data of this monitoring data.

4. The outburst early warning method based on continuous rise quantitative characterization of gas emission amount according to claim 1, characterized in that In step 4), the quantitative characterization of the difference Δx between the highest value and the lowest value of the monitoring data within the unit time Δx = max|x1,......,xi| - min|x1,......,x i | ≥ S(2) In the above formula, Δx is the difference between the highest value and the lowest value of the monitoring data within the unit time; S is the critical value of the difference between the highest value and the lowest value of the monitoring data within the unit time.

5. The outburst early warning method based on quantitative characterization of continuous increase in gas emission amount according to claim 3, characterized in that In step 5), the quantitative characterization of the total number of times L2 that the monitoring data within the unit time is less than all the subsequent data of this monitoring data If x j <min|x j+1 ,......,x i |, then h(x j ) = 1; Otherwise, h(x j ) = 0; h is the cumulative function of the decreasing times of the minimum outburst volume; In the above formula, L2 is the total number of times that the monitoring data in a unit time is less than all the subsequent data of this monitoring data; m2 is the critical value of the total number of times that the monitoring data in a unit time is less than all the subsequent data of this monitoring data.

6. A prominent early warning method based on quantitative characterization of continuous increase in gas emission amount according to claim 1, 2, 3 or 4, characterized in that Since the monitoring data is dynamically statistically analyzed, L1, Δx, and L2 in a unit time are not fixed and change with the change of the monitoring data.

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