CN110599033A - Dynamic prediction method for goaf spontaneous combustion danger by introducing update factor - Google Patents
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Abstract
The invention provides a dynamic prediction method for a gob spontaneous combustion danger by introducing an update factor, and relates to the technical field of coal mine safety engineering. The invention provides a dynamic prediction method for spontaneous combustion danger of residual coal in a coal mine goaf, which is characterized in that the dynamic prediction method for spontaneous combustion danger of the goaf is provided for understanding the spontaneous combustion danger degree of the residual coal in the goaf, understanding the relation between the spontaneous combustion of the residual coal and the influence factors thereof in the dynamic stoping process of a working face and dynamically predicting the spontaneous combustion danger, and the dynamic prediction method for the spontaneous combustion of the goaf is improved by CRITIC correction G2-TOPSIS. An optimized decision model is established by introducing an Euclidean distance function, a G2 weighting method is modified by adopting a CRITIC weighting method, the comprehensive weight of each index is calculated, and then an update factor is introduced to obtain the dynamic weight. A G2 weighting method based on the principle of approaching the drive function of the theory of ideal solution ordering method (TOPSIS) is used for establishing a G2-TOPSIS goaf spontaneous combustion dynamic evaluation model, analyzing the closeness and finally predicting the spontaneous combustion risk degree of the goaf.
Description
Technical Field
The invention relates to the technical field of coal mine safety engineering, in particular to a dynamic prediction method for a goaf spontaneous combustion risk by introducing an update factor.
Background
China always belongs to a large country for producing and using coal, and accounts for 90% -94% of the total number of mine fires according to the statistics of the number of spontaneous combustion fires of major coal mines in large and medium-sized countries, wherein spontaneous combustion of residual coal in a gob accounts for 60% of all spontaneous combustion fires, so that serious economic loss and casualties are caused.
Spontaneous combustion of coal is a complex physical and chemical process, the basic reaction mechanism and process of which are always the research focus in the field of coal mining, and in general, spontaneous combustion of coal can be expressed as a slow oxidation process, and the occurrence of spontaneous combustion of coal is also carried out under certain conditions, and mainly comprises the following steps: the coal with smaller oxidizable particles under the low-temperature condition has better chemical activity due to larger specific surface area; has good ventilation so as to provide oxygen for supplying oxidation reaction; there are physical conditions capable of storing reaction heat, but analysis of these influences in practical engineering is difficult, some extreme conditions bring great difficulty to monitoring and analysis, on the other hand, for the spontaneous combustion of the residual coal, considerable data are also accumulated under the condition of allowing manual and equipment detection, how to use these data to find out the relationship between the spontaneous combustion of the coal and its influence factors, and then predict the position where the spontaneous combustion of the residual coal may occur, and finally achieve the purpose of advance prevention and control, which should be a problem of research with great emphasis.
Some researches are carried out on spontaneous combustion prediction analysis of residual coal in the goaf at present, but the prediction methods and results are influenced too much by subjective factors of evaluation experts, and the expert evaluations with different experiences and levels are different.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a goaf spontaneous combustion danger dynamic prediction method introducing an update factor, and provides an improved CRITIC information content correction G2 weighting method, which avoids the defect that a single weighting method cannot reflect main and objective information simultaneously on the basis of considering both index data variability and conflict, introduces a weight update factor aiming at the characteristic of goaf environment dynamic change, establishes a G2-TOPSIS goaf spontaneous combustion dynamic prediction model according to a superior-inferior solution distance method (TOPSIS), and analyzes the possibility and tendency of spontaneous combustion of the residual coal in a certain coal mine goaf by using the model.
The technical scheme adopted by the invention is as follows: a gob spontaneous combustion danger dynamic prediction method introducing an update factor; the method comprises the following steps:
step 1: taking the coal carbonization metamorphism degree, the coal oxidation reduction ignition point temperature difference, the oxygen absorption capacity of coal, the CO unit temperature rise rate, the geological structure, the coal seam buried depth, the coal seam thickness, the coal seam inclination angle, the air leakage strength, the air leakage time length, the surrounding rock temperature, the hole sealing mode, the hole sealing length, the hole sealing depth and the hole sealing material as evaluation indexes of a test point spontaneous combustion danger single index, dividing the evaluation indexes into a subjective index and an objective index, and determining the objective evaluation index weight by utilizing an improved CRITIC method;
step 2: determining subjective index weight by using a G2 weighting method;
and step 3: correcting the G2 method by using an improved CRITIC method, and determining the comprehensive weight of the evaluation index;
improving each evaluation index by CRITIC information quantity CkIs recorded as [ C ] in the confidence interval1k，C2k]And wherein k is 1, 2, …, m, calculating a value interval of the ratio of the importance degrees of the two indexes by utilizing the ratio of the upper limit and the lower limit of the confidence interval of the improved CRITIC information quantity, and replacing a rational assignment interval in a G2 method, namely:
in the formula: rkmIs a confidence interval;
calculating the comprehensive weight w of the kth evaluation index improved CRITIC correction G2 interval judgment index as follows:
and 4, step 4: introducing an update factor to determine the dynamic weight of each index, wherein the calculation formula is as follows:
in the formula, ws(0) The standard weight of each index; w is the composite weight; q (0) is the initial value of the spontaneous combustion index score; delta q is the absolute value of the difference between the score value of each index and the initial value of each index at a certain moment;updating the factor for the weight; w is aDDynamic weighting for each index;
and 5: constructing an initial judgment matrix;
step 6: determining positive and negative ideal solutions to calculate similarity;
and 7: constructing a G2-TOPSIS dynamic evaluation model, and utilizing the average value of the dynamic weight of the indexes of the criterion layerAnd combining the judgment matrix V formed by scoring each index of the alignment side layer after reordering by a method based on machine change CRITIC improvement G2 to obtain a comprehensive judgment result L of the judgment object:
and 8: the method comprises the steps of establishing an extraction drill hole spontaneous combustion danger grade, dividing the extraction drill hole spontaneous combustion danger into 5 grades, wherein the grade I, the grade II, the grade III, the grade IV, the grade V and the grade I are basically impossible to occur, the grade II, the grade III, the grade IV and the grade V are extremely possible to occur, the grade assignment is [0, 100], the higher the index assignment is, the lower the spontaneous combustion danger of the extraction drill hole caused by the index is, and the lower the spontaneous combustion probability of the extraction drill hole is.
The specific steps of the step 1 are as follows:
step 1.1: calculating the improved CRITIC information content contained in each evaluation index, wherein the formula is as follows:
in the formula: ckAmount of improved CRITIC information, σ, for the kth evaluation indexkIs the standard deviation of the kth evaluation index, ukIs the mean value of the k-th evaluation index,is a quantized value of the degree of mutual influence of the kth index and other indexes, tikThe correlation coefficients are evaluation indexes i and k;
step 1.2: calculating the weight w of each evaluation indexckThe formula is as follows:
the specific steps of the step 2 are as follows:
step 2.1: according to the influence weight of the evaluation target, namely the preference coefficient, the original index set { u } containing the information content of the evaluation index in the step 1.1iThe m indexes are reordered from large to small according to importance, and the index set after the ordering is marked as { u }i1，…，uik，…，uimIn which ui1U is the most important indeximIs the least important index, and is used for the evaluation index uikAnd uimRatio of importance akAnd (3) calculating:
ak＝uik/uim，k＝1，2，…，m-1；
step 2.2: when the information amount of a certain evaluation index is insufficient, a cannot be calculatedkThe exact value, when given to akOne range of values, denoted as Dk，Dk＝[d1k，d2k]Passing section length e (D)k) And a section midpoint n (D)k) The calculation of (D) yields an interval mapping function φ εk) Wherein:
e(Dk)＝d2k-d1k
φε(Dk)＝n(Dk)+εe(Dk)
in the formula: epsilon is a risk attitude factor (| epsilon | is less than or equal to 0.5), and when epsilon is less than or equal to 0.5 and less than or equal to 0, epsilon is a conservative type; when epsilon is 0, the compound is a neutral type; when epsilon is more than or equal to 0 and less than or equal to 0.5, the model is a risk model;
if { DkCalculating the weight w of the kth index by the G2 method if the value is accurately assignedGkThe following are:
the specific steps of the step 5 are as follows:
step 5.1: let the multifactor judge object set A ═ A1，A2，…，AmAnd R is a judgment index attribute set for measuring the quality of the object1，R2，…，RnEach judgment object A in the judgment object set AiA vector [ a ] composed of n index attribute values of (i ═ 1, 2, …, m)i1，ai2，，ain]The object A can be uniquely characterized by using the vector as a point in an n-dimensional spacei(ii) a Evaluation index aijThe j index attribute value representing the i evaluation object, wherein i belongs to [1, m ]]，j∈[1，n]If the initial judgment matrix is:
step 5.2: because the dimensions of each index are different, the index attribute values need to be normalized when making a decision, and the values are all converted to 0, 1]On the interval; evaluation matrix B for normalization processing by TOPSIS methodj＝(bij)m×nWherein:
step 5.3: construction of a weighted evaluation matrix ZijThe comprehensive weight W of the evaluation index obtained by the improved CRITIC correction G2 method forms a weight matrix W, and the normalized evaluation matrix B obtained by the TOPSIS methodijMultiplying to obtain a weighted comprehensive evaluation matrix:
the specific steps of the step 6 are as follows:
step 6.1: according to the weighted comprehensive judgment matrix ZijAcquiring positive and negative ideal solutions of an evaluation target:
f+＝{(max bj|j∈J+)，(min bj|j∈J-)}
f-＝{(min bj|j∈J+)，(max bj|j∈J-)}
in the formula: j. the design is a square+As a benefit type indicator, J-As a cost index, f+To evaluate a positive ideal solution of the target, f-A negative ideal solution for the evaluation objective;
step 6.2: calculating the Euclidean distance between each evaluation target and the ideal solution:
in the formula: si +Represents the positive Euclidean distance, S, between the evaluation target and the ideal solutioni -Representing the negative Euclidean distance between the evaluation target and the ideal solution;
step 6.3: calculating the relative closeness of each judgment result to the optimal value:
in the formula: n is a radical ofi +Representing relative closeness;the larger the value is, the closer the evaluation object is to the ideal solution, the better the evaluation object is, and the evaluation objects are sorted according to the relative closeness degree to form a decision basis.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1) the G2 weighting method is corrected by using the improved CRITIC information quantity, so that the defect that subjective and objective information cannot be simultaneously reflected by a single weighting method can be avoided on the basis of considering both index data variability and conflict.
2) Aiming at the characteristic of dynamic change of the goaf environment, a weight updating factor is introduced, a G2-TOPSIS goaf spontaneous combustion dynamic prediction model is established according to a top-bottom solution distance method (TOPSIS), and the evaluation result can analyze the possibility of spontaneous combustion of the goaf in the current state and the dynamic change trend of spontaneous combustion danger, so that the purpose of preventing in advance is achieved.
Drawings
FIG. 1 shows the calculation process of G2-TOPSIS dynamic prediction model.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
A dynamic prediction method for a goaf spontaneous combustion risk by introducing an update factor is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1: taking the coal carbonization metamorphism degree, the coal oxidation reduction ignition point temperature difference, the oxygen absorption capacity of coal, the CO unit temperature rise rate, the geological structure, the coal seam buried depth, the coal seam thickness, the coal seam inclination angle, the air leakage strength, the air leakage time length, the surrounding rock temperature, the hole sealing mode, the hole sealing length, the hole sealing depth and the hole sealing material as evaluation indexes of a test point spontaneous combustion danger single index, dividing the evaluation indexes into a subjective index and an objective index, and determining the objective evaluation index weight by utilizing an improved CRITIC method;
step 1.1: calculating the improved CRITIC information content contained in each evaluation index, wherein the formula is as follows:
in the formula: ckAmount of improved CRITIC information, σ, for the kth evaluation indexkIs the standard deviation of the kth evaluation index, ukIs the mean value of the k-th evaluation index,is a quantized value of the degree of mutual influence of the kth index and other indexes, tikThe correlation coefficients are evaluation indexes i and k;
step 1.2: calculating the weight w of each evaluation indexckThe formula is as follows:
step 2: determining subjective index weight by using a G2 weighting method;
step 2.1: according to the influence weight of the evaluation target, namely the preference coefficient, the original index set { u } containing the information content of the evaluation index in the step 1.1iM indices in the theory according to importanceReordering from big to small, and recording the index set after the reordering as { ui1，…，uik，…，uimIn which ui1U is the most important indeximIs the least important index, and is used for the evaluation index uikAnd uimRatio of importance akAnd (3) calculating:
ak＝uik/uim，k＝1，2，…，m-1； (3)
step 2.2: when the information amount of a certain evaluation index is insufficient, a cannot be calculatedkThe exact value, when given to akOne range of values, denoted as Dk，Dk＝[d1k，d2k]Passing section length e (D)k) And a section midpoint n (D)k) The calculation of (D) yields an interval mapping function φ εk) Wherein:
e(Dk)＝d2k-d1k (4)
φε(Dk)＝n(Dk)+εe(Dk) (6)
in the formula: epsilon is a risk attitude factor (| epsilon | is less than or equal to 0.5), and when epsilon is less than or equal to 0.5 and less than or equal to 0, epsilon is a conservative type; when epsilon is 0, the compound is a neutral type; when epsilon is more than or equal to 0 and less than or equal to 0.5, the model is a risk model;
if { DkCalculating the weight w of the kth index by the G2 method if the value is accurately assignedGkThe following are:
and step 3: correcting the G2 method by using an improved CRITIC method, and determining the comprehensive weight of the evaluation index;
improving each evaluation index by CRITIC information quantity CkIs recorded as [ C ] in the confidence interval1k，C2k]Wherein k is 1, 2, …, m, calculating the value range of the ratio of the importance degree of the two indexes by using the ratio of the upper limit and the lower limit of the confidence range of the improved CRITIC information content,instead of rational assignment intervals in the G2 method, namely:
in the formula: rkmIs a confidence interval;
calculating the comprehensive weight w of the kth evaluation index improved CRITIC correction G2 interval judgment index as follows:
and 4, step 4: introducing an update factor to determine the dynamic weight of each index, wherein the calculation formula is as follows:
in the formula, ws(0) The standard weight of each index; w is the composite weight; q (0) is the initial value of the spontaneous combustion index score; delta q is the absolute value of the difference between the score value of each index and the initial value of each index at a certain moment;updating the factor for the weight; w is aDDynamic weighting for each index;
and 5: constructing an initial judgment matrix;
step 5.1: let the multifactor judge object set A ═ A1，A2，…，AmAnd R is a judgment index attribute set for measuring the quality of the object1，R2，…，RnEach judgment object A in the judgment object set AiA vector [ a ] composed of n index attribute values of (i ═ 1, 2, …, m)i1，ai2，，ain]The object A can be uniquely characterized by using the vector as a point in an n-dimensional spacei(ii) a Evaluation index aijThe j index attribute value representing the i evaluation object, wherein i belongs to [1, m ]]，j∈[1，n]If the initial judgment matrix is:
step 5.2: because the dimensions of each index are different, the index attribute values need to be normalized when making a decision, and the values are all converted to 0, 1]On the interval; evaluation matrix B for normalization processing by TOPSIS methodij＝(bij)m×nWherein:
step 5.3: construction of a weighted evaluation matrix ZijThe comprehensive weight W of the evaluation index obtained by the improved CRITIC correction G2 method forms a weight matrix W, and the normalized evaluation matrix B obtained by the TOPSIS methodijMultiplying to obtain a weighted comprehensive evaluation matrix:
step 6: determining positive and negative ideal solutions to calculate similarity;
step 6.1: according to the weighted comprehensive judgment matrix ZijAcquiring positive and negative ideal solutions of an evaluation target:
f+＝{(max bj|j∈J+)，(min bj|j∈J-)} (16)
f-＝{(min bj|j∈J+)，(max bj|j∈J-)} (17)
in the formula: j. the design is a square+As a benefit type indicator, J-As a cost index, f+To evaluate a positive ideal solution of the target, f-A negative ideal solution for the evaluation objective;
step 6.2: calculating the Euclidean distance between each evaluation target and the ideal solution:
in the formula: si +Represents the positive Euclidean distance, S, between the evaluation target and the ideal solutioni -Representing the negative Euclidean distance between the evaluation target and the ideal solution;
step 6.3: calculating the relative closeness of each judgment result to the optimal value:
in the formula: n is a radical ofi +Representing relative closeness;the larger the value is, the closer the evaluation object is to the ideal solution, the better the evaluation object is, and the evaluation objects are sorted according to the relative closeness degree to form a decision basis.
And 7: constructing a G2-TOPSIS dynamic evaluation model, and utilizing the average value of the dynamic weight of the indexes of the criterion layerAnd combining the judgment matrix V formed by scoring each index of the alignment side layer after reordering by a method based on machine change CRITIC improvement G2 to obtain a comprehensive judgment result L of the judgment object:
and 8: the spontaneous combustion danger level of the extraction drill hole is established, the spontaneous combustion danger of the extraction drill hole is divided into 5 levels, the level I, the level II, the level III, the level IV and the level V are unlikely to occur, the assignment of each level is [0, 100], the higher the assignment of each index is, the smaller the spontaneous combustion danger of the extraction drill hole caused by the index is, the smaller the spontaneous combustion probability of the extraction drill hole is, and the spontaneous combustion ignition evaluation level and the value range of the goaf are shown in the table 1.
TABLE 1 spontaneous combustion and ignition comment grade and value range of goaf
The following is a detailed description with reference to the examples:
taking a certain coal mine in Shanxi as an example, the spontaneous combustion risk degree of the residual coal in the goaf is scored for 3 times. The individual indicators of auto-ignition risk were reordered using the CRITIC method and the 15 reordered individual indicators were scored as shown in table 2.
TABLE 2 initial value of index evaluation after scoring and improvement
Since each evaluation index weight is calculated by using the G2 weighting method modified by improving CRITIC according to the interval scored in table 2, and the risk attitude factor ∈ is known to be 0.25, the index score of the criterion layer is substituted into formula (9), and the result is shown in table 3, and the weight matrix W is obtained.
TABLE 3 evaluation index weight values
w＝diag(0.052 0.090 0.081 0.071 0.062 0.071 0.043 0.052 0.088 0.081 0.081 0.043 0.073 0.052 0.062)
TABLE 4 index update factor and dynamic weight
The engineering attribute weight, weight update factor and dynamic weight of each index are calculated by using the formulas (10-12), and the calculation results are shown in table 4.
And establishing an initial judgment matrix A according to the index of each criterion layer.
Normalizing the obtained product by adopting a formula (14) to obtain Bij。
Dynamic weight matrix of evaluation index obtained by G2 methodMultiplying the normalized evaluation matrix Bij obtained by the TOPSIS method to form a weighted comprehensive evaluation matrix Zij：
The obtained weighted comprehensive evaluation matrix ZijSubstituting equations (16), (17) yields the positive and negative ideal solutions of the evaluation target:
f+＝(0.037 0.045 0.073 0.045 0.019 0.033 0.03 0.024 0.027 0.082 0.079 0.026 0.036 0.032 0.054)
f-＝(0.028 0.032 0.055 0.038 0.014 0.017 0.022 0.019 0.02 0.051 0.079 0.022 0.027 0.024 0.046)
calculating Euclidean distance between each scoring data and an ideal value by using formulas (18) and (19) according to the obtained positive and negative ideal solutions:
and according to the obtained Euclidean distance, calculating the relative closeness of each score to each index of the criterion layer by using a formula (20):
according to the calculation of the comprehensive weight and the relative closeness, the 2-time and 3-time scoring judgment results are better. G2-TOPSIS evaluation is carried out on the 2 nd and 3 rd scoring results, and dynamic weight is addedAnd multiplying the evaluation matrix V formed by scoring to obtain a G2-TOPSIS dynamic evaluation result L.
And obtaining a goaf spontaneous combustion risk evaluation result L (69.349 ∈ (60, 70)) by a G2-TOPSIS dynamic evaluation model formula (21), predicting that the goaf spontaneous combustion risk level is III level, conforming to the natural ignition condition of the actual goaf, and indicating that the model prediction result is suitable for field application
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions as defined in the appended claims.
Claims (5)
1. A dynamic prediction method for a gob spontaneous combustion danger by introducing an update factor is characterized in that: the method comprises the following steps:
step 1: taking the coal carbonization metamorphism degree, the coal oxidation reduction ignition point temperature difference, the oxygen absorption capacity of coal, the CO unit temperature rise rate, the geological structure, the coal seam buried depth, the coal seam thickness, the coal seam inclination angle, the air leakage strength, the air leakage time length, the surrounding rock temperature, the hole sealing mode, the hole sealing length, the hole sealing depth and the hole sealing material as evaluation indexes of a test point spontaneous combustion danger single index, dividing the evaluation indexes into a subjective index and an objective index, and determining the objective evaluation index weight by utilizing an improved CRITIC method;
step 2: determining subjective index weight by using a G2 weighting method;
and step 3: correcting the G2 method by using an improved CRITIC method, and determining the comprehensive weight of the evaluation index;
improving each evaluation index by CRITIC information quantity CkIs recorded as [ C ] in the confidence interval1k，C2k]And wherein k is 1, 2, …, m, calculating a value interval of the ratio of the importance degrees of the two indexes by utilizing the ratio of the upper limit and the lower limit of the confidence interval of the improved CRITIC information quantity, and replacing a rational assignment interval in a G2 method, namely:
in the formula: rkmIs a confidence interval;
calculating the comprehensive weight w of the kth evaluation index improved CRITIC correction G2 interval judgment index as follows:
and 4, step 4: introducing an update factor to determine the dynamic weight of each index, wherein the calculation formula is as follows:
in the formula, ws(0) The standard weight of each index; w is the composite weight; q (0) is the initial value of the spontaneous combustion index score; delta q is the absolute value of the difference between the score value of each index and the initial value of each index at a certain moment;updating the factor for the weight; w is aDDynamic weighting for each index;
and 5: constructing an initial judgment matrix;
step 6: determining positive and negative ideal solutions to calculate similarity;
and 7: constructing a G2-TOPSIS dynamic evaluation model, and utilizing the average value of the dynamic weight of the indexes of the criterion layerAnd combining the judgment matrix V formed by scoring each index of the alignment side layer after reordering by a method based on machine change CRITIC improvement G2 to obtain a comprehensive judgment result L of the judgment object:
and 8: the method comprises the steps of establishing an extraction drill hole spontaneous combustion danger grade, dividing the extraction drill hole spontaneous combustion danger into 5 grades, wherein the grade I, the grade II, the grade III, the grade IV, the grade V and the grade I are basically impossible to occur, the grade II, the grade III, the grade IV and the grade V are extremely possible to occur, the grade assignment is [0, 100], the higher the index assignment is, the lower the spontaneous combustion danger of the extraction drill hole caused by the index is, and the lower the spontaneous combustion probability of the extraction drill hole is.
2. The dynamic prediction method for the spontaneous combustion risk of the goaf introduced with the update factor according to claim 1, characterized in that:
the specific steps of the step 1 are as follows:
step 1.1: calculating the improved CRITIC information content contained in each evaluation index, wherein the formula is as follows:
in the formula: ckAmount of improved CRITIC information, σ, for the kth evaluation indexkIs the standard deviation of the kth evaluation index, ukIs the mean value of the k-th evaluation index,is a quantized value of the degree of mutual influence of the kth index and other indexes, tikThe correlation coefficients are evaluation indexes i and k;
step 1.2: calculating the weight w of each evaluation indexckThe formula is as follows:
3. the dynamic prediction method for the spontaneous combustion risk of the goaf introduced with the update factor according to claim 1, characterized in that:
the specific steps of the step 2 are as follows:
step 2.1: according to the influence weight of the evaluation target, namely the preference coefficient, the original index set { u } containing the information content of the evaluation index in the step 1.1iThe m indexes are reordered from large to small according to importance, and the index set after the ordering is marked as { u }il，…，uik，…，uimIn which uilU is the most important indeximIs the least important index, and is used for the evaluation index uikAnd uimRatio of importance akAnd (3) calculating:
ak＝uik/uim，k＝1，2，…，m-1；
step 2.2: when the information amount of a certain evaluation index is insufficient, a cannot be calculatedkThe exact value, when given to akOne range of values, denoted as Dk，Dk＝[d1k，d2k]Passing section length e (D)k) And a section midpoint n (D)k) The calculation of (D) yields an interval mapping function φ εk) Wherein:
e(Dk)＝d2k-d1k
φε(Dk)＝n(Dk)+εe(Dk)
in the formula: epsilon is a risk attitude factor (| epsilon | is less than or equal to 0.5), and when epsilon is less than or equal to 0.5 and less than or equal to 0, epsilon is a conservative type; when epsilon is 0, the compound is a neutral type; when epsilon is more than or equal to 0 and less than or equal to 0.5, the model is a risk model;
if { DkCalculating the weight w of the kth index by the G2 method if the value is accurately assignedGkThe following are:
4. the dynamic prediction method for the spontaneous combustion risk of the goaf introduced with the update factor according to claim 1, characterized in that:
the specific steps of the step 5 are as follows:
step 5.1: let the multifactor judge object set A ═ A1，A2，…，AmAnd R is a judgment index attribute set for measuring the quality of the object1，R2，…，RnEach judgment object A in the judgment object set AiA vector [ a ] composed of n index attribute values of (i ═ 1, 2, …, m)i1，ai2，，ain]The object A can be uniquely characterized by using the vector as a point in an n-dimensional spacei(ii) a Evaluation index aijThe j index attribute value representing the i evaluation object, wherein i belongs to [1, m ]]，j∈[1，n]If the initial judgment matrix is:
step 5.2: because the dimensions of each index are different, the index attribute value is required to be subjected to decision makingNormalizing to convert the values to [0, 1%]On the interval; evaluation matrix B for normalization processing by TOPSIS methodij＝(bij)m×nWherein:
step 5.3: construction of a weighted evaluation matrix ZijThe comprehensive weight W of the evaluation index obtained by the improved CRITIC correction G2 method forms a weight matrix W, and the normalized evaluation matrix B obtained by the TOPSIS methodijMultiplying to obtain a weighted comprehensive evaluation matrix:
5. the dynamic prediction method for the spontaneous combustion risk of the goaf introduced with the update factor according to claim 1, characterized in that:
the specific steps of the step 6 are as follows:
step 6.1: according to the weighted comprehensive judgment matrix ZjAcquiring positive and negative ideal solutions of an evaluation target:
f+＝{(max bj|j∈J+)，(min bj|j∈J-)}
f-＝{(min bj|j∈J+)，(max bj|j∈J-)}
in the formula: j. the design is a square+As a benefit type indicator, J-As a cost index, f+To evaluate a positive ideal solution of the target, f-A negative ideal solution for the evaluation objective;
step 6.2: calculating the Euclidean distance between each evaluation target and the ideal solution:
in the formula:represents the positive Euclidean distance between the evaluation target and the ideal solution,representing the negative Euclidean distance between the evaluation target and the ideal solution;
step 6.3: calculating the relative closeness of each judgment result to the optimal value:
in the formula:representing relative closeness;the larger the value is, the closer the evaluation object is to the ideal solution, the better the evaluation object is, and the evaluation objects are sorted according to the relative closeness degree to form a decision basis.
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