CN105488730A - Fire risk monitoring method of easy firing point at goaf - Google Patents

Abstract

The present invention relates to a fire risk monitoring method of an easy firing point at a goaf. The method provided by the invention comprises the following steps: determining the temperature correlation coefficients of environment temperatures and fire risks according to the environment temperature T0 at the next period and the concentration C0 of fire gases generated by coal and according to the distance between a temperature sensor configured to detect the environment temperature and the easy firing point, and determining the concentration correlation coefficients of the concentration and the fire risk according to the distance of a concentration detection device configured to detect the concentration and the easy firing point to respectively determine an environment temperature predictive index and a concentration predictive index; finally calculating the fire synthesis predictive index W, W is equal to the sum of the environment temperature predictive index and the concentration predictive index; W is larger, the fire risk is higher. The fire risk monitoring method of an easy firing point at a goaf is helpful for improving the accuracy of fire prediction.

Description

A kind of goaf easy ignition point condition of a fire risk monitoring method

Technical field

The present invention relates to mine condition of a fire monitoring technical field, be specifically related to a kind of goaf easy ignition point condition of a fire risk monitoring method.

Background technology

Mine fire is one of Major Natural Disasters in coal production, and Conflagration Caused by Coal Self-ignition is the principal mode of mine fire, account for 90% of mine fire sum, particularly China extensively adopts Longwall Top Coal Caving Method in recent years, exhaust technology is widelyd popularize in gas control, while production efficiency increases substantially and greatly reduces with gas emission, cause goaf leave over coal more, leak out serious, freely burning fire is taken place frequently.In recent years the coal-face more or less a hundred that China's State owned coal mine is closed because of fire every year, the annual coal amount of freezing caused because of closed workplace is all more than ten million ton.What closed workplace often made up to ten million unit comprehensively adopts, fully mechanized coal face is enclosed in flame range, and a large amount of coal freezes because of flame range, and rational development plan and mining sequence are broken, and brings huge economic loss and major accident hidden danger to mine.Mine fire has become one of principal element of restriction mine safety production and development.

Colliery scene fire extinguishing experience for many years shows, spontaneous combustion of coal is once form fire, and fire-fighting work not only needs to drop into a large amount of manpower and materials, and extinguishing effect is not good, and therefore the preventing and controlling of Conflagration Caused by Coal Self-ignition focus on prevention.In order to carry out the prevention work of coal, prediction accurately must be carried out to coal spontaneous combustion.The developing stage that the generating process of Conflagration Caused by Coal Self-ignition can be divided into slow oxidation stage, accelerated oxidation stage different with the vigorous oxidation stage three, different phase correspond to different gaseous product kinds and concentration.Use Tube Bundle Monitoring System to detect the gas componant of underground coal mine, identifying generation and the development degree thereof of coal spontaneous combustion according to the existence of gas componant and concentration change feature thereof, is the most widely used method of current coal spontaneous combustion prediction.But make monitoring system cannot effectively monitor goaf gas componant by the faults such as beam tube blocking, gas leakage that affect of underground coal mine water burst, dust.

Along with the development of Fiber Optic Pyrometer, market is proposed the mining fire monitoring system having gas composition monitoring and optical fiber temperature-measurement function concurrently, this system measures the concentration of the environment temperature in fire monitoring region and the inflammable gas of coal generation respectively by temperature-measuring optical fiber (temperature sensor) and beam tube.

After coal-face puts into production, the factor that coal winning technology, ventilation, ventilating management method etc. affect spontaneous combustion of coal is generally relatively stable, therefore the position of place in goaf being conducive to most being formed spontaneous combustion in goaf is also relatively stable, colliery scene engineering technical personnel can obtain the relative position of easy ignition point in goaf in long-term fire extinguishing practice observation, because easily the place of spontaneous combustion occurs in goaf ignition point often the earliest, therefore predict that the condition of a fire of easy ignition point is significant.

Because personnel cannot enter goaf, the mode that can only be imported by pre-buried or roadway construction boring nearby arranges temperature-measuring optical fiber and the beam tube of limited quantity, along with the easy ignition point in propelling goaf of coal-face also moves thereupon, therefore most time temperature-measuring optical fiber and beam tube air intake opening are not positioned at easy ignition point, but be positioned at the neighbouring position of easy ignition point, temperature-measuring optical fiber and beam tube air intake opening can be arranged on same position, also different positions can be arranged on, as shown in Figure 1, no matter whether both are arranged on same position, the position unified definition all both being arranged installation is monitoring point, namely for detecting the monitoring point of goaf environment temperature and gas concentration near easy ignition point, the situation of easy ignition point is characterized by the environment temperature and gas concentration detecting this monitoring point place.Therefore the method that the condition of a fire proposing a kind of utilization parameter commute ignition point of position monitoring near easy ignition point carries out forecasting has practical significance.

The top board of coal mine gob is progressively caving with old power station, the rock mass be caving not only can cause destruction to a certain degree to the temperature-measuring optical fiber and beam tube being arranged in goaf, and understand conduction and the diffusion of blocking temperature and gas, difficulty is caused to Accurate Prediction condition of a fire developing stage, in addition water burst, the factors such as dust also can impact the monitoring of temperature and gas parameter, a kind of noise data can got rid of because environmental impact and monitoring instrument self stability cause is proposed, the method that the condition of a fire of the parameter commute ignition point of position monitoring carries out forecasting near easy ignition point is utilized to have realistic meaning.

Summary of the invention

The object of this invention is to provide a kind of goaf easy ignition point condition of a fire risk monitoring method.

For achieving the above object, the solution of the present invention comprises a kind of goaf easy ignition point condition of a fire risk monitoring method, comprises the following steps:

(1) concentration of the environment temperature at monitoring point place and the inflammable gas of coal generation, is obtained;

(2) the environment temperature T in concentration prediction next period of the inflammable gas, produced according to described environment temperature and coal ₀with the concentration C of the inflammable gas that coal produces ₀;

(3), according to the temperature association coefficient being used for the temperature sensor of testing environment temperature and the distance determination environment temperature easily between ignition point and condition of a fire risk, according to the concentration correlation coefficient for the concentration detection apparatus of detectable concentration and the distance determination concentration easily between ignition point and condition of a fire risk;

(4), according to the described temperature association coefficient determined and described environment temperature T ₀computing environment temperature prediction index, according to the described concentration correlation coefficient determined and described concentration C ₀calculating concentration prediction index;

(5), calculate condition of a fire integrated forecasting index W, described condition of a fire integrated forecasting index W equals described environment temperature prediction index and described concentration prediction index sum; Described condition of a fire integrated forecasting index W is larger, and condition of a fire risk is higher.

The account form of described condition of a fire integrated forecasting index W is: W=T ₁r ₁+ C ₁r ₂, wherein, T ₁for environment temperature T ₀the value obtained after normalized, C ₁for concentration C ₀the value obtained after normalized, R ₁for described temperature association coefficient, R ₂for described concentration correlation coefficient, T ₁r ₁for described environment temperature prediction index, C ₁r ₂for described concentration prediction index.

As 0 < W < W ₁time, condition of a fire developing stage is normal; Work as W ₁≤ W < W ₂time, condition of a fire developing stage is self-heating; Work as W ₂≤ W < W ₃time, condition of a fire developing stage is spontaneous combustion; Wherein, described W ₁, W ₂, W ₃for setting threshold value, and 0≤W ₁< W ₂< W ₃≤ 1.

Described T ₁account form be: calculate T ₀the ratio of environment temperature when catching fire with coal, is denoted as T ₁;

Described C ₁account form be: C ₁=C ₀/ C _from, described C _fromfor the concentration of the described inflammable gas produced corresponding during spontaneous combustion of coal.

Described temperature association coefficients R ₁account form be: R ₁=(L ₁-x)/L ₁, L ₁for the possible ultimate range of temperature sensor and easy ignition point, x is the actual range of temperature sensor and easy ignition point; Described concentration correlation coefficient R ₂account form be: R ₂=(L ₂-y)/L ₂, L ₂for the possible ultimate range of concentration detection apparatus and easy ignition point, y is the actual range of concentration detection apparatus and easy ignition point.

Further comprising the steps of between described step (1) and step (2): employing filtering algorithm rejects the noise in the concentration data of the environment temperature at the described monitoring point place detected and the inflammable gas of coal generation.

Use the environment temperature T in next period described in gray prediction GM (1,1) model prediction ₀with the concentration C of the inflammable gas that coal produces ₀.

Realize the environment temperature T in next period described in the model prediction of described use gray prediction GM (1,1) ₀with the concentration C of the inflammable gas that coal produces ₀means comprise the following steps:

1), formula is utilized raw data is added up, wherein, x ⁽⁰⁾(1), x ⁽⁰⁾(2) ..., x ⁽⁰⁾n () is raw data, x ⁽¹⁾(1), x ⁽¹⁾(2) ..., x ⁽¹⁾(n) one-accumulate generation data for calculating;

2), One first-order ordinary differential equation is set up: this One first-order ordinary differential equation is Grey models GM (1,1), and a, u are model parameter, wherein:

$\left[\begin{array}{c}a\\ u\end{array}\right]={\left(B^{T}B\right)}^{-1}{B}^T{Y}_n$

$B=\left[\begin{array}{cc}-\frac{1}{2}\left(x^{\left(1\right)}\right(1)+x^{\left(1\right)}\left(2\right))& 1\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(2)+x^{\left(1\right)}\left(3\right))& 1\\ .& .\\ .& .\\ .& .\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(n-1)+x^{\left(1\right)}\left(n\right))& 1\end{array}\right]$

Y＝(x ⁽⁰⁾(2),x ⁽⁰⁾(3),…,x ⁽⁰⁾(n)) ^T；

3), the model parameter a that will try to achieve, u substitutes into described One first-order ordinary differential equation, obtains GM (1, the 1) forecast model of discretize:

${\hat{x}}^{\left(1\right)}\left(k\right)=\left(x^{\left(0\right)}\right(1)-\frac{u}{a})e^{-a(k-1)}+\frac{u}{a};$

4), pass through the environment temperature T in next period described in prediction ₀with the concentration C of the inflammable gas that coal produces ₀.

Described inflammable gas is carbon monoxide.

The invention provides in the easy ignition point condition of a fire risk monitoring method of a kind of goaf, these the two kinds of Prediction Parameters of concentration of the inflammable gas produced by integrated environment temperature and coal carry out condition of a fire prediction, when original temperature or gas monitoring system cannot normally be monitored, can contribute to by this monitoring method the accuracy improving condition of a fire prediction.And, the corresponding data at the monitoring point place in next period is predicted by the concentration of the inflammable gas produced according to the environment temperature detected and coal, and the correlation coefficient that near building according to corresponding data, monitoring parameter and easy ignition point are monitored, then condition of a fire integrated forecasting index is calculated according to correlation coefficient, the prediction index that last basis calculates carries out the prediction of the condition of a fire, contributes to the accuracy improving condition of a fire prediction.

And, in the present invention, the weighted sum of concentration prediction value and environment temperature predicted value is utilized to carry out condition of a fire prediction, when some prediction weighted values arrive certain value, Accurate Prediction can be carried out to the condition of a fire, and also can carry out Accurate Prediction to the condition of a fire when two prediction weighted value sums arrive certain value, improve the accuracy of condition of a fire prediction further.

In addition, the method that the present invention uses is simple, and practical.

Accompanying drawing explanation

Fig. 1 is that schematic diagram arranged by temperature-measuring optical fiber and beam tube;

Fig. 2 is the process flow diagram of goaf easy ignition point condition of a fire risk monitoring method.

Embodiment

Below in conjunction with accompanying drawing, the present invention will be further described in detail.

As shown in Figure 2, goaf provided by the invention easy ignition point condition of a fire risk monitoring method, comprises the following steps:

(1) concentration of the environment temperature at monitoring point place and the inflammable gas of coal generation, is detected; Coal is under the effect of outside heat, kind and the concentration of different inflammable gas products can be produced to the process of spontaneous combustion from heating, because in the inflammable gas of generation, chief component is carbon monoxide, so the concentration detecting carbon monoxide is here used as the monitoring raw data of condition of a fire risk.

In addition, in order to reject the noise data in primary monitoring data, to improve the accuracy of prediction, in the method, after step (1), back to back step (2) is: adopt filtering average algorithm, also referred to as filtering algorithm, rejects the noise in the data of the environment temperature at the monitoring point place detected and the carbonomonoxide concentration of coal generation.

This filtering algorithm is: the mean value calculating raw sensor data, then rejects the data being greater than the first setting multiple of this mean value in raw sensor data, and rejects the data of the second setting multiple being less than this mean value.Such as: monitor in the last period 11 temperature datas obtaining for 25,25,27,28,29,31,40,31,32,34,34}, the mean value of these 11 data is 30.5, reject the data being greater than mean value 30% and being less than mean value 30%, because data 40 are greater than 30% of average 30.5, therefore using 40 as noise data reject, remaining 10 temperature datas be 25,25,27,28,29,31,31,32,34,34}.For another example: monitor in the last period 11 gas concentration data obtaining for 15,16,17,17,17,18,19,19,20,28,20}, according to identical method using 28 as noise data reject, remaining 10 temperature datas be 15,16,17,17,17,18,19,19,20,20}.

(3) the environment temperature T at the monitoring point place in gray prediction GM (1,1) model prediction next period, is used ₀with the concentration C of the carbon monoxide that coal produces ₀.

Specific as follows:

3.1, raw data is added up:

Setting tool has probabilistic raw data to be x ⁽⁰⁾(1), x ⁽⁰⁾(2) ..., x ⁽⁰⁾n (), carries out Accumulating generation (AGO) according to formula (1) to raw data, obtain one-accumulate and generate data x ⁽¹⁾(1), x ⁽¹⁾(2) ..., x ⁽¹⁾(n).

$x^{\left(1\right)}\left(n\right)=\underset{k=1}{\overset{n}{\&Sigma;}}{x}^{\left(0\right)}\left(k\right)---\left(1\right)$

3.2, gray prediction GM (1,1) model is set up:

One first-order ordinary differential equation:

$\frac{{\mathrm{dx}}^{\left(1\right)}}{dt}+{\mathrm{ax}}^{\left(1\right)}=u---\left(2\right)$

Be Grey models GM (1,1), wherein a, u are model parameter, can obtain according to Least Square Method.Wherein:

$\left[\begin{array}{c}a\\ u\end{array}\right]={\left(B^{T}B\right)}^{-1}{B}^T{Y}_n---\left(3\right)$

$B=\left[\begin{array}{cc}-\frac{1}{2}\left(x^{\left(1\right)}\right(1)+x^{\left(1\right)}\left(2\right))& 1\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(2)+x^{\left(1\right)}\left(3\right))& 1\\ .& .\\ .& .\\ .& .\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(n-1)+x^{\left(1\right)}\left(n\right))& 1\end{array}\right]---\left(4\right)$

Y＝(x ⁽⁰⁾(2),x ⁽⁰⁾(3),…,x ⁽⁰⁾(n)) ^T(5)

3.3, the differential equation is solved:

The model parameter a tried to achieve, u are substituted into One first-order ordinary differential equation (2), and Xie Zhi, namely obtains GM (1, the 1) forecast model of discretize:

${\hat{x}}^{\left(1\right)}\left(k\right)=\left(x^{\left(0\right)}\right(1)-\frac{u}{a})e^{-a(k-1)}+\frac{u}{a}---\left(6\right)$

3.4, the predicted value of raw data is solved:

By (6) formula try to achieve be one-accumulate generate predicted value, try to achieve the predicted value of raw data by (7):

Such as: the prediction using above-mentioned gray prediction GM (1,1) model and follow-up formula to carry out temperature data is specially:

3.1, within the last period, monitoring 10 temperature datas obtained is: 25,25,27,28,29,31,31,32,34,34}, according to formula (1), Accumulating generation (AGO) is carried out to 10 temperature datas, obtain one-accumulate generate data 25,50,77,105,134,165,196,228,262,296}.

3.2, according to (4), (5) two formulas are tried to achieve:

B＝[-37.51；-63.51；-911；-119.51；-149.51；-180.51；-2121；-2451；-2791]；

Y＝[25；27；28；29；31；31；32；34；34]。

Utilize Matlab to carry out matrix operation and obtain a=-0.027371, u=24.469132, a, u are substituted into (2) formula.

3.3, (2) formula that solves obtains GM (1, the 1) forecast model of discretize:

${\hat{x}}^{\left(1\right)}\left(k\right)=918.98e^{0.027371(k-1)}-893.98$

3.4, try to achieve according to the forecast model of this discretize the predicted value that one-accumulate generates data, and obtain the temperature prediction value T in next period according to (7) formula ₀it is 33.5 °.

The prediction using above-mentioned gray prediction GM (1,1) model and follow-up formula to carry out gas data is specially:

Within the last period, monitor 10 the carbonomonoxide concentration data obtained is: 15,16,17,17,17,18,19,19,20,20}, according to method identical with temperature prediction, the forecasting of Gas Concentration value C in next period can be obtained ₀be 21.3.

(4), respectively to the environment temperature T at the monitoring point place in next period ₀with the concentration C of the carbon monoxide that coal produces ₀be normalized, correspondence obtains T ₁and C ₁.

Wherein, the environment temperature T at the monitoring point place to next period is realized ₀the mode be normalized is: calculate T ₀the ratio of environment temperature when catching fire with coal, is denoted as T ₁.Environment temperature when coal catches fire is the environment critical temperature value when coal catches fire, conventional coal is stone coal, bituminous coal and brown coal, the temperature threshold value that different coals is corresponding different, the environment temperature critical value that stone coal is corresponding is 400 °, the environment temperature critical value that bituminous coal is corresponding is 350 °, and the environment temperature critical value that brown coal are corresponding is 300 °.

Realize the concentration C to the carbon monoxide that the coal in next period produces ₀the mode be normalized is: C ₁=C ₀/ C _from, C _fromfor the concentration of the carbon monoxide produced corresponding during spontaneous combustion of coal, the empirical data that the concentration of carbon monoxide corresponding during this spontaneous combustion of coal arrives from on-the-spot long-term observation.

Such as: the something lost coal in goaf is stone coal, the environment temperature critical value namely during coal spontaneous combustion is 400 °, then the temperature prediction value T in next period ₀normalization result be T ₁=33.5/400=0.084, during spontaneous combustion of coal, the concentration of the corresponding carbon monoxide produced is 100ppm, then the carbonomonoxide concentration predicted value C in next period ₀normalization result be C ₁=21.3/100=0.213.

(5) condition of a fire prediction correlation coefficient R, is calculated ₁, R ₂.

Wherein, R ₁account form be: R ₁=(L ₁-x)/L ₁, L ₁for for the temperature sensor of testing environment temperature data and the possible ultimate range of the easy ignition point of the condition of a fire, x is the actual range of temperature sensor and the easy ignition point of the condition of a fire.In the above-mentioned possible ultimate range mentioned may be not unclear, because temperature sensor might not in identical position with easy ignition point, but due to apart from excessive words, the data that temperature sensor detects just cannot be effective to the situation detecting easy ignition point, so, both necessarily have a maximum distance, and this distance effectively to detect the ultimate range of the situation of easy ignition point, and this maximum distance is just L ₁, so this possibility ultimate range also can be described as ultimate range.

R ₂account form be: R ₂=(L ₂-y)/L ₂, L ₂for the possible ultimate range of beam tube air intake opening and the easy ignition point of the condition of a fire, y is the actual range of beam tube air intake opening and the easy ignition point of the condition of a fire.In like manner, in the above-mentioned possible ultimate range mentioned may also be not unclear, because beam tube air intake opening might not in identical position with easy ignition point, but due to apart from excessive words, the data that beam tube air intake opening detects just cannot be effective to the situation detecting easy ignition point, so both necessarily have a maximum distance, this distance effectively to detect the ultimate range of the situation of easy ignition point, and this maximum distance is just L _2,so this possibility ultimate range also can be described as ultimate range.

Such as: for the temperature sensor of testing environment temperature data and the possible ultimate range L of the easy ignition point of the condition of a fire ₁for 30m, the actual range x of temperature sensor and the easy ignition point of the condition of a fire is 10m, then temperature association coefficients R ₁=(L ₁-x)/L ₁=0.68; The possible ultimate range L of beam tube air intake opening and the easy ignition point of the condition of a fire ₂for 30m, the actual range y of beam tube air intake opening and the easy ignition point of the condition of a fire is 15m, then gas concentration correlation coefficient R ₂=(L ₂-y)/L ₂=0.5.

(6), according to T ₁, C ₁, R ₁and R ₂calculate condition of a fire integrated forecasting index W, according to this index W, the condition of a fire risk of the easy ignition point in goaf is predicted.

The account form of condition of a fire integrated forecasting index W is: W=T ₁r ₁+ C ₁r ₂, as 0 < W < W ₁time, condition of a fire developing stage is normal; Work as W ₁≤ W < W ₂time, condition of a fire developing stage is self-heating; Work as W ₂≤ W < W ₃time, condition of a fire developing stage is spontaneous combustion; Wherein, W ₁, W ₂, W ₃for setting threshold value, and 0≤W ₁< W ₂< W ₃≤ 1.As a concrete embodiment, W ₁be 0.4, W ₂be 0.7, W ₃be 1, namely as 0 < W < 0.4, condition of a fire developing stage is normal; As 0.4≤W < 0.7, condition of a fire developing stage is self-heating; As 0.7≤W < 1, condition of a fire developing stage is spontaneous combustion.

Such as: condition of a fire integrated forecasting index W=T ₁r ₁+ C ₁r ₂=0.084*0.68+0.213*0.5=0.164, due to 0 < W < 0.4, judges that ignition situation is normal.

Be presented above concrete embodiment, but the present invention is not limited to described embodiment.Basic ideas of the present invention are above-mentioned basic scheme, and for those of ordinary skill in the art, according to instruction of the present invention, designing the model of various distortion, formula, parameter does not need to spend creative work.The change carried out embodiment without departing from the principles and spirit of the present invention, amendment, replacement and modification still fall within the scope of protection of the present invention.

Claims (9)

1. the easy ignition point condition of a fire in a goaf risk monitoring method, is characterized in that, comprise the following steps:

(1) concentration of the environment temperature at monitoring point place and the inflammable gas of coal generation, is obtained;

(2) the environment temperature T in concentration prediction next period of the inflammable gas, produced according to described environment temperature and coal ₀with the concentration C of the inflammable gas that coal produces ₀;

(3), according to the temperature association coefficient being used for the temperature sensor of testing environment temperature and the distance determination environment temperature easily between ignition point and condition of a fire risk, according to the concentration correlation coefficient for the concentration detection apparatus of detectable concentration and the distance determination concentration easily between ignition point and condition of a fire risk;

(4), according to the described temperature association coefficient determined and described environment temperature T ₀computing environment temperature prediction index, according to the described concentration correlation coefficient determined and described concentration C ₀calculating concentration prediction index;

(5), calculate condition of a fire integrated forecasting index W, described condition of a fire integrated forecasting index W equals described environment temperature prediction index and described concentration prediction index sum; Described condition of a fire integrated forecasting index W is larger, and condition of a fire risk is higher.

2. the easy ignition point condition of a fire in goaf according to claim 1 risk monitoring method, is characterized in that, the account form of described condition of a fire integrated forecasting index W is: W=T ₁r ₁+ C ₁r ₂, wherein, T ₁for environment temperature T ₀the value obtained after normalized, C ₁for concentration C ₀the value obtained after normalized, R ₁for described temperature association coefficient, R ₂for described concentration correlation coefficient, T ₁r ₁for described environment temperature prediction index, C ₁r ₂for described concentration prediction index.

3. the easy ignition point condition of a fire in goaf according to claim 2 risk monitoring method, is characterized in that, as 0 < W < W ₁time, condition of a fire developing stage is normal; Work as W ₁≤ W < W ₂time, condition of a fire developing stage is self-heating; Work as W ₂≤ W < W ₃time, condition of a fire developing stage is spontaneous combustion; Wherein, described W ₁, W ₂, W ₃for setting threshold value, and 0≤W ₁< W ₂< W ₃≤ 1.

4. the easy ignition point condition of a fire in goaf according to claim 2 risk monitoring method, is characterized in that, described T ₁account form be: calculate T ₀the ratio of environment temperature when catching fire with coal, is denoted as T ₁;

Described C ₁account form be: C ₁=C ₀/ C _from, described C _fromfor the concentration of the described inflammable gas produced corresponding during spontaneous combustion of coal.

5. the easy ignition point condition of a fire in goaf according to claim 2 risk monitoring method, is characterized in that, described temperature association coefficients R ₁account form be: R ₁=(L ₁-x)/L ₁, L ₁for the possible ultimate range of temperature sensor and easy ignition point, x is the actual range of temperature sensor and easy ignition point; Described concentration correlation coefficient R ₂account form be: R ₂=(L ₂-y)/L ₂, L ₂for the possible ultimate range of concentration detection apparatus and easy ignition point, y is the actual range of concentration detection apparatus and easy ignition point.

6. the easy ignition point condition of a fire in goaf according to claim 1 risk monitoring method, it is characterized in that, further comprising the steps of between described step (1) and step (2): employing filtering algorithm rejects the noise in the concentration data of the environment temperature at the described monitoring point place detected and the inflammable gas of coal generation.

7. the easy ignition point condition of a fire in goaf according to claim 1 risk monitoring method, is characterized in that, uses the environment temperature T in next period described in gray prediction GM (1,1) model prediction ₀with the concentration C of the inflammable gas that coal produces ₀.

8. the easy ignition point condition of a fire in goaf according to claim 7 risk monitoring method, is characterized in that, realizes the environment temperature T in next period described in the model prediction of described use gray prediction GM (1,1) ₀with the concentration C of the inflammable gas that coal produces ₀means comprise the following steps:

1), formula is utilized raw data is added up, wherein, x ⁽⁰⁾(1), x ⁽⁰⁾(2) ..., x ⁽⁰⁾n () is raw data, x ⁽¹⁾(1), x ⁽¹⁾(2) ..., x ⁽¹⁾(n) one-accumulate generation data for calculating;

2), One first-order ordinary differential equation is set up: this One first-order ordinary differential equation is Grey models GM (1,1), and a, u are model parameter, wherein:

$\left[\begin{array}{c}a\\ u\end{array}\right]={\left(B^{T}B\right)}^{-1}{B}^T{Y}_n$

$B=\left[\begin{array}{cc}-\frac{1}{2}\left(x^{\left(1\right)}\right(1)+x^{\left(1\right)}(2\left)\right)& 1\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(2)+x^{\left(1\right)}(3\left)\right)& 1\\ .& .\\ .& .\\ .& .\\ -\frac{1}{2}\left(x^{\left(1\right)}\right(n-1)+x^{\left(1\right)}(n\left)\right)& 1\end{array}\right]$

Y＝(x ⁽⁰⁾(2),x ⁽⁰⁾(3),…,x ⁽⁰⁾(n)) ^T；

3), the model parameter a that will try to achieve, u substitutes into described One first-order ordinary differential equation, obtains GM (1, the 1) forecast model of discretize:

${\hat{x}}^{\left(1\right)}\left(k\right)=\left(x^{\left(0\right)}\right(1)-\frac{u}{a})e^{-a(k-1)}+\frac{u}{a};$

4), pass through the environment temperature T in next period described in prediction ₀with the concentration C of the inflammable gas that coal produces ₀.

9. the easy ignition point condition of a fire in goaf according to claim 1 risk monitoring method, is characterized in that, described inflammable gas is carbon monoxide.