Gas explosion risk assessment method based on combined empowerment

Abstract

The invention relates to the technical field of gas explosion risk assessment, in particular to a gas explosion risk assessment method based on combined weighting. The method comprises the following steps: constructing a gas explosion risk evaluation index system; respectively calculating the subjective weight and objective weight of each index, constructing a multi-objective optimization function with the minimum subjective and objective deviation and the minimum marginal contribution difference as targets, and solving the combined weight of each index by using a particle swarm optimization algorithm and a game theory method; dividing risk grades, and constructing a standard cloud model of the gas explosion risk; constructing a comprehensive cloud model of the gas explosion risk; and taking the risk level corresponding to the standard cloud closest to the comprehensive cloud model as the gas explosion risk level of the coal mine to be evaluated. According to the gas explosion risk assessment method based on combined weighting, the risk level of the coal mine to be assessed can be accurately determined, and the accuracy of the assessment result is improved.

Description

Technical Field

[0001] This invention relates to the field of gas explosion risk assessment technology, and more specifically, to a gas explosion risk assessment method based on combined weighting. Background Technology

[0002] In order to prevent accidents, protect the lives of personnel, and achieve safe production and sustainable operation of coal mines, it is necessary to assess the risk of coal mine gas explosions to provide a basis for scientific decision-making.

[0003] When assessing the risk of gas explosions in coal mines, a subjective and objective weighting method is usually used to coordinate the allocation of weights. However, traditional methods suffer from subjective bias and objective imbalance, resulting in poor accuracy of the assessment results. Summary of the Invention

[0004] The purpose of this invention is to provide a gas explosion risk assessment method based on combined weighting, so as to improve the technical problem of poor accuracy of assessment results in gas explosion risk assessment methods in related technologies.

[0005] The gas explosion risk assessment method based on combined weighting provided by this invention includes: A gas explosion risk assessment index system is constructed, which contains multiple indicators; The subjective and objective weights of each indicator are calculated separately. A multi-objective optimization function is constructed with the objectives of "minimizing the subjective-objective deviation" and "minimizing the marginal contribution difference". The combined weights of each indicator are solved using the particle swarm optimization algorithm and game theory. Risk levels are divided and the range of cloud droplet values ​​for each risk level is defined. Based on the improved golden section method, the expected value, entropy and hyperentropy of multiple standard clouds corresponding one-to-one with multiple risk levels are calculated as digital features of multiple standard clouds to construct a standard cloud model for gas explosion risk. Based on the numerical characteristics of each indicator of the coal mine to be evaluated and the combined weight of each indicator, a comprehensive cloud model of gas explosion risk is constructed. The risk level corresponding to the standard cloud that is closest to the comprehensive cloud model is taken as the gas explosion risk level of the coal mine to be evaluated.

[0006] As one possible implementation method, the gas explosion risk includes two dimensions: risk probability and degree of danger, and the standard cloud model and the integrated cloud model are two-dimensional cloud models.

[0007] As one possible implementation, the method further includes: numbering multiple indicators from 1 to n; The multi-objective optimization function is: ,in, , Let be the combination coefficient of the i-th indicator. , , ; As a penalty factor, ; The subjective weight of the i-th indicator. Let be the objective weight of the i-th indicator; The combined weight of the i-th indicator; The marginal contribution of the subjective weight of the i-th indicator. The marginal contribution of the objective weight of the i-th indicator.

[0008] As one possible implementation method, the calculation of the subjective and objective weights of each indicator includes: The subjective weights of each indicator are calculated using the analytic hierarchy process (AHP); and / or the objective weights of each indicator are calculated using the CRITIC method.

[0009] As one possible implementation method, the construction of a comprehensive cloud model for gas explosion risk based on the numerical characteristics of various indicators of the coal mine to be evaluated and the combined weights of these indicators includes: Each indicator of the coal mine to be evaluated is scored independently; Calculate the numerical characteristics of each indicator based on the scoring data; Based on the numerical characteristics and combined weights of each indicator, the numerical characteristics are aggregated layer by layer to the corresponding criteria layer; wherein, the criteria layer includes several categories, and multiple indicators affect several of the categories respectively; Through comprehensive calculations, the digital characteristics of the comprehensive cloud model of gas explosion risk are obtained, in order to construct the comprehensive cloud model.

[0010] As one possible implementation, the method further includes: The similarity between the integrated cloud model and multiple standard clouds is calculated respectively, and the standard cloud corresponding to the largest calculation result is taken as the standard cloud that is most similar to the integrated cloud model.

[0011] As one possible implementation method, the formula for calculating the proximity is: ,in, The expected risk probability of the integrated cloud model. The expected level of danger for the integrated cloud model; The expected risk probability of the standard cloud. The expected level of danger for the standard cloud.

[0012] As one possible implementation, the method further includes: The cloud maps of each of the standard clouds and the cloud map of the integrated cloud model are drawn respectively. The cloud map of the integrated cloud model is compared with the cloud maps of each of the standard clouds. The cloud map of the standard cloud that is closest to the cloud map of the integrated cloud model is selected as the cloud map of the standard cloud that is most similar to the integrated cloud model.

[0013] As one possible implementation method, the various risk levels include low risk, lower risk, medium risk, higher risk, and high risk.

[0014] As one possible implementation method, the cloud droplet value of the standard cloud corresponding to the low risk ranges from 0 to 1.6, with an expected value of 0.8, an entropy of 0.27, and a hyperentropy of 0.03. The cloud droplet value of the standard cloud corresponding to the lower risk ranges from 1.6 to 4.8, with an expected value of 3.2, an entropy of 0.53, and a hyperentropy of 0.05. The cloud droplet value of the standard cloud corresponding to the medium risk ranges from 4.8 to 7.6, with an expected value of 6.2, an entropy of 0.70, and a hyperentropy of 0.02. The cloud droplet value of the standard cloud corresponding to the higher risk ranges from 7.6 to 8.8, with an expected value of 8.2, an entropy of 0.20, and a hyperentropy of 0.02. The cloud droplet value of the standard cloud corresponding to the high risk ranges from 8.8 to 10, with an expected value of 9.4, an entropy of 0.20, and a hyperentropy of 0.02.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: In this invention, the smaller the function value of the multi-objective optimization function, the better it satisfies the two objective conditions ("minimum subjective and objective deviation" and "minimum marginal contribution difference"), and the more reasonable the allocation of subjective and objective weights. The Particle Swarm Optimization (PSO) algorithm can effectively handle complex constraints in the multi-objective optimization function and possesses superior global search and boundary constraint capabilities. It can avoid common local optimum traps in weight allocation problems, ensuring the feasibility of the solution. Therefore, by constructing a multi-objective optimization function and combining it with game theory based on PSO, subjective and objective factors are comprehensively considered. This allows for adaptive solution of the optimal combination parameters to obtain the combined weights of each indicator, achieving dynamic coordination and balanced optimization of weight allocation. This improves the accuracy of the subsequently constructed comprehensive cloud model of gas explosion risk, thereby accurately determining the risk level of the coal mine to be evaluated and improving the accuracy of the evaluation results.

[0016] Therefore, the gas explosion risk assessment method based on combined weighting provided by this invention can provide a scientific basis for gas explosion risk identification and evaluation, and also provide technical support for the formulation of precise policies and differentiated management strategies. It has high theoretical value and practical significance for improving the inherent safety level of coal mines and building risk-controllable mines. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 A first schematic flowchart of the gas explosion risk assessment method based on combined weighting provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the gas explosion risk assessment index system in an embodiment of the present invention; Figure 3 This invention provides the risk level classification rules and handling results for gas explosions in this embodiment. Figure 4a This is a front view of a standard cloud map of gas explosion risk in an embodiment of the present invention; Figure 4b This is a top view of a standard cloud map of gas explosion risk in an embodiment of the present invention; Figure 5 This is a second schematic flowchart of the gas explosion risk assessment method based on combined weighting provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0021] Figure 1 A gas explosion risk assessment method based on combined weighting, provided as an embodiment of the present invention, includes: S102, Construct a gas explosion risk assessment index system, which contains multiple indicators; S104, calculate the subjective and objective weights of each indicator respectively, construct a multi-objective optimization function with the objectives of "minimizing the subjective and objective deviation" and "minimizing the marginal contribution difference", and use particle swarm optimization algorithm and game theory to solve the combined weights of each indicator; S106, classify risk levels and define the range of cloud droplet values ​​for each risk level; based on the improved golden section method, calculate the expectation, entropy and hyperentropy of multiple standard clouds that correspond one-to-one with multiple risk levels, as digital features of multiple standard clouds, in order to construct a standard cloud model for gas explosion risk. S108. Based on the numerical characteristics of each indicator of the coal mine to be evaluated and the combined weight of each indicator, a comprehensive cloud model of gas explosion risk is constructed. S110 will be the risk level of the standard cloud that is closest to the integrated cloud model, which will be used as the gas explosion risk level of the coal mine to be evaluated.

[0022] In this embodiment, the smaller the function value of the multi-objective optimization function, the better it satisfies the two objective conditions ("minimum subjective and objective deviation" and "minimum marginal contribution difference"), and the more reasonable the allocation of subjective and objective weights. The Particle Swarm Optimization (PSO) algorithm can effectively handle complex constraints in the multi-objective optimization function and possesses superior global search and boundary constraint capabilities. It can avoid common local optimum traps in weight allocation problems, ensuring the feasibility of the solution. Therefore, by constructing a multi-objective optimization function and combining it with game theory based on PSO, subjective and objective factors are comprehensively considered. This allows for adaptive solution of the optimal combination parameters to obtain the combined weights of each indicator, achieving dynamic coordination and balanced optimization of weight allocation. This improves the accuracy of the subsequently constructed comprehensive cloud model of gas explosion risk, thereby accurately determining the risk level of the coal mine to be evaluated and improving the accuracy of the evaluation results.

[0023] Therefore, the gas explosion risk assessment method based on combined weighting provided in this embodiment can provide a scientific basis for gas explosion risk identification and evaluation, and also provide technical support for the formulation of precise policies and differentiated management strategies. It has high theoretical value and practical significance for improving the inherent safety level of coal mines and building risk-controllable mines.

[0024] The aforementioned gas explosion risk can be categorized into two dimensions: risk probability and hazard level. Accordingly, both the standard cloud model and the integrated cloud model are constructed as two-dimensional cloud models, evaluating gas explosion risk from these two dimensions. The risk probability dimension focuses on analyzing the likelihood of disaster-causing factors such as gas accumulation and ignition sources; the hazard level dimension focuses on examining the severity of potential casualties and economic losses caused by the explosion. By constructing a two-dimensional "probability-consequence" evaluation system, high-risk aspects can be systematically identified, providing a scientific basis for developing targeted prevention and control measures. This overcomes the limitations of traditional single-dimensional models, accurately reflects the gas explosion risk level, and further improves the accuracy of the assessment results.

[0025] This paper evaluates the risk level of gas explosions by dividing the uncertainty into two dimensions: risk probability and hazard severity. It combines qualitative concepts with quantitative data transformation. Compared to traditional cloud models, the two-dimensional cloud model in this embodiment adds an entropy-hyper-entropy relationship, allowing the evaluation results to consider not only the mean of the indicators but also dynamic fluctuations. This gives it an advantage in handling comprehensive evaluation problems of uncertainty under the combined influence of two factors.

[0026] The construction process of the two-dimensional cloud model is as follows: Suppose the expected value of a random function F following a two-dimensional normal distribution is... , These represent the average levels of evaluation indicators X (risk probability) and Y (severity of harm), respectively. , Let X and Y represent the entropy of evaluation indicators X and Y, respectively, reflecting the dispersion of the indicators and characterizing uncertainty; hyperentropy. , Used to measure the volatility of entropy and improve the dynamic adaptability of the model.

[0027]

[0028] In the formula, — Represents the coordinates of the cloud droplet. For membership degree, cloud droplet The resulting cloud model is a two-dimensional cloud model.

[0029] The method in this embodiment also includes numbering multiple indicators from 1 to n.

[0030] The formula for the above multi-objective optimization function is as follows: .

[0031] in, , Let be the combination coefficient of the i-th indicator. Used to determine the proportion of subjective weighting. Used to determine the proportion of objective weights and set constraints. , , To ensure that the sum of the subjective and objective weights of each indicator is 1, and that both the subjective and objective weights contribute at least 20% of the total, the requirement is that the subjective weight contribution and the objective weight contribution are both retained. Set constraints for the penalty factor. . The subjective weight of the i-th indicator. Let be the objective weight of the i-th indicator. The combined weight of the i-th indicator is calculated using the following formula: ; The marginal contribution of the subjective weight of the i-th indicator is calculated using the following formula: ; The marginal contribution of the objective weight of the i-th indicator is calculated using the following formula: .

[0032] It should be noted that the above multi-objective optimization function The smaller the value, the better it meets the two objective conditions of "minimum subjective and objective deviation" and "minimum marginal contribution difference". Correspondingly, the allocation of subjective and objective weights is more reasonable.

[0033] Step S102 above, which calculates the subjective and objective weights of each indicator, includes: using the Analytic Hierarchy Process (AHP) to calculate the subjective weights of each indicator. The AHP is a systematic, hierarchical, multi-criteria decision-making method. This method decomposes complex problems into hierarchical structures, uses expert judgment to compare indicators pairwise, and ultimately quantifies the relative importance of each element, providing a scientific basis for decision-making. The operational steps are as follows: ① Construct the judgment matrix for n 1 evaluation index, construct a judgment matrix ; where matrix elements Representative indicators i For indicators j The extent of the impact , , The scoring rules for the degree of impact are as follows: A, when the indicator i With indicators j When equally important, ; B, when the indicator i Comparison Indicators j When it is slightly important, ; C, when the indicator i Comparison Indicators j When it is obviously important, ; D, when the indicator i Relative indicators j When the importance falls between cases A and B, ; E, when the indicator i Relative indicators j When the importance falls between cases B and C, .

[0034] Indicators i Relative indicators j The more important, the The larger the value, ② Subjective weighting of indicators W 1. Calculation The eigenvector method is used to find the largest eigenvalue of the judgment matrix. The corresponding feature vectors, after normalization, yield the weights of each indicator. ; ③ Consistency check To ensure the reliability and rationality of the judgment logic, the consistency ratio needs to be calculated. CR The calculation formula is: , Here, CI is the consistency index, RI is the random consistency index, which is calculated based on the average CI value of randomly generated judgment matrices and is related to the order of the judgment matrices. n is the number of indices. The smaller the CR value of the judgment matrix, the better the consistency of the judgment matrix. Generally, when CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable; otherwise, the judgment matrix needs to be corrected.

[0035] Step S102 above, which calculates the subjective and objective weights of each indicator, includes: calculating the objective weights of each indicator using the CRITIC method. The CRITIC method overcomes subjective weighting bias by quantifying the information intensity and conflict between indicators, and is suitable for evaluating complex systems with multiple coupled indicators. Information intensity is measured by the standard deviation, which indicates the indicator's ability to distinguish samples; a larger standard deviation indicates a stronger ability to differentiate between samples. Conflict is reflected by the correlation coefficient between indicators, which indicates the degree of duplicate information; higher correlation results in a greater weight penalty.

[0036] The above step S108 specifically includes the following steps: independently scoring each indicator of the coal mine to be evaluated; calculating the numerical characteristics of each indicator based on the scoring data; and weighting and aggregating the numerical characteristics and combined weights of each indicator layer by layer to the corresponding numerical characteristics of the criterion layer; wherein, the criterion layer includes several categories, and multiple indicators affect several categories respectively; and through comprehensive calculation, obtaining the numerical characteristics of the comprehensive cloud model of gas explosion risk to construct the comprehensive cloud model.

[0037] Specifically, based on systems safety engineering theory, this study identifies key factors of gas explosion risk from a capability-system-environment perspective. These influencing factors are categorized into four aspects: management factors, equipment factors, environmental factors, and personnel factors. Combining the requirements of relevant national safety regulations such as the "Coal Mine Safety Regulations" and the "Detailed Rules for the Prevention and Control of Coal and Gas Outbursts," a coal mine gas explosion risk assessment index system is constructed, forming four categories (primary indicators) and 18 indicators (secondary indicators) at the criterion level. Figure 2 As shown in Table 1, the weights of each indicator and category in the risk probability dimension of a gas explosion are calculated based on this, and the weights of each indicator and category in the hazard degree dimension of a gas explosion are shown in Table 2.

[0038] Table 1. Probability Weighting Table for Gas Explosion Risk

[0039] Table 2. Weighting Table of Gas Explosion Hazard Levels

[0040] As shown in Table 1, under the probability dimension of gas explosion risk, the three indicators with the highest weights are: gas anomaly identification skills, coal seam gas content, and gas monitoring systems. Gas anomaly identification skills indicate that early identification and prediction of abnormal gas behavior by personnel reduces the probability of risk occurrence; gas monitoring systems enable real-time and comprehensive monitoring of gas concentration, preventing gas accumulation; and coal seam gas content reflects the magnitude of the risk at the gas source—the higher the gas content, the greater the potential for explosion. These three factors work together at the front end of the risk chain, determining the likelihood of an explosion accident.

[0041] As shown in Table 2, under the dimension of gas explosion hazard level, the three indicators with the highest weight are: personnel operation standardization, emergency response capability, and gas prevention and control system. High operation standardization can effectively reduce the probability of accidents worsening due to misoperation or violations; good emergency response capability is related to whether the situation can be quickly and effectively controlled after an accident occurs, and whether casualties can be prevented from escalating; the gas prevention and control system represents the company's ability to pre-control various risks at the system level, and inadequate implementation of the system can easily lead to management failure and risk accumulation.

[0042] The method provided in this embodiment may further include: calculating the proximity of the integrated cloud model to multiple standard clouds, and taking the standard cloud corresponding to the largest calculation result as the standard cloud most similar to the integrated cloud model. This method can determine the standard cloud most similar to the integrated cloud model through the specific value of the proximity. The proximity can be used to calculate the similarity between the integrated cloud model and the standard clouds. The closer the comprehensive evaluation cloud level is to a certain standard cloud level, the higher the proximity value between the two. Therefore, the gas explosion risk level can be more accurately judged by the level of proximity value.

[0043] The formula for calculating closeness is: ,in, The mathematical expectation of the risk probability of the integrated cloud model. The mathematical expectation of the degree of danger in the comprehensive cloud model; The mathematical expectation of the risk probability of a standard cloud. The mathematical expectation of the danger level of a standard cloud.

[0044] The method provided in this embodiment may further include the following steps: drawing cloud maps of each standard cloud and a cloud map of the integrated cloud model respectively; comparing the cloud map of the integrated cloud model with the cloud maps of each standard cloud; and selecting the cloud map of the standard cloud that is closest to the cloud map of the integrated cloud model as the cloud map of the standard cloud that is most similar to the integrated cloud model. This method can determine the standard cloud that is most similar to the integrated cloud model through cloud maps.

[0045] Preferably, the risk levels can be divided into low risk, lower risk, medium risk, higher risk, and high risk to meet the needs.

[0046] The formula for calculating the expected value of a standard cloud is: The formula for calculating the entropy of a standard cloud is: The formula for calculating the hyperentropy of a standard cloud is: In the formula, and These represent the maximum and minimum values ​​within the standard cloud titer range, respectively.

[0047] See Figure 3 The risk level of a gas explosion can be determined according to the following rules: A. The cloud droplet value of the standard cloud corresponding to low risk ranges from 0 to 1.6, with an expected value of 0.8, an entropy of 0.27, and a hyperentropy of 0.03. At this time, the probability and degree of danger of gas explosion are both low.

[0048] B, the standard cloud droplet value corresponding to lower risk ranges from 1.6 to 4.8, with an expected value of 3.2, an entropy of 0.53, and a hyperentropy of 0.05; at this time, the probability and degree of danger of gas explosion are both low.

[0049] C, the cloud droplet value of the standard cloud corresponding to medium risk ranges from 4.8 to 7.6, with an expected value of 6.2, an entropy of 0.70, and a hyperentropy of 0.07; at this point, the probability and degree of danger of gas explosion are both medium.

[0050] D, the standard cloud droplet value range for higher risk is 7.6~8.8, the expected value is 8.2, the entropy is 0.20, and the hyperentropy is 0.02; at this time, the probability and degree of danger of gas explosion are both high.

[0051] E, the cloud droplet value of the standard cloud corresponding to high risk ranges from 8.8 to 10, with an expected value of 9.4, an entropy of 0.20, and a hyperentropy of 0.02; at this time, the probability and degree of danger of gas explosion are both high.

[0052] The digital characteristics of each standard cloud are shown in Table 3. Standard cloud maps of gas explosion risk can be drawn using Python software (e.g., ...). Figure 4a and Figure 4bAs shown in the figure, the lower and medium risks account for the largest proportion; the expected values ​​of each level are all located near the golden section point of the interval; the magnitude of the entropy value reflects the uncertainty characteristics of the level. The higher and high risk levels reflect the prudent assessment of serious consequences through smaller entropy values, while the lower and medium risk levels objectively reflect the uncertainty of the transition state through larger entropy values, which is consistent with the actual situation.

[0053] Table 3. Cloud Characteristics of Gas Explosion Risk Assessment Standards

[0054] See Figure 3 The handling strategies for each risk level are as follows: A. When the risk level of a gas explosion is low, normal production continues. B. When the risk level of a gas explosion is low to medium, increase risk defense. C. When the risk level of a gas explosion is medium, strict monitoring and active rectification should be carried out to prevent the risk from escalating. D. When the risk level of a gas explosion is medium to high, immediate rectification should be carried out to reduce the risk. E. When the risk level of a gas explosion is high, take immediate measures to reduce the risk.

[0055] Figure 5 This is a schematic flowchart of a gas explosion risk assessment method based on combined weighting, which is provided as an embodiment of the present invention. The method provides detailed steps from three aspects: construction of evaluation index system, combined weighting of index system, and construction and application of rating model.

[0056] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

[0057] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0058] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A gas explosion risk assessment method based on combined weighting, characterized in that, include: A gas explosion risk assessment index system is constructed, which contains multiple indicators; The subjective and objective weights of each indicator are calculated separately. A multi-objective optimization function is constructed with the objectives of "minimizing the subjective-objective deviation" and "minimizing the marginal contribution difference". The combined weights of each indicator are solved using the particle swarm optimization algorithm and game theory. Divide the risk levels and define the cloud droplet value range for each risk level; Based on the improved golden section method, the expected value, entropy, and hyperentropy of multiple standard clouds corresponding one-to-one with multiple risk levels are calculated as digital features of multiple standard clouds to construct a standard cloud model for gas explosion risk. Based on the numerical characteristics of each indicator of the coal mine to be evaluated and the combined weight of each indicator, a comprehensive cloud model of gas explosion risk is constructed. The risk level corresponding to the standard cloud that is closest to the comprehensive cloud model is taken as the gas explosion risk level of the coal mine to be evaluated.

2. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The gas explosion risk includes two dimensions: risk probability and degree of danger. The standard cloud model and the integrated cloud model are two-dimensional cloud models.

3. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The method further includes: numbering multiple indicators from 1 to n; The multi-objective optimization function is: ,in, , Let be the combination coefficient of the i-th indicator. , , ; As a penalty factor, ; The subjective weight of the i-th indicator. Let be the objective weight of the i-th indicator; The combined weight of the i-th indicator; The marginal contribution of the subjective weight of the i-th indicator. The marginal contribution of the objective weight of the i-th indicator.

4. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The calculation of the subjective and objective weights of each indicator includes: The subjective weights of each indicator are calculated using the analytic hierarchy process (AHP); and / or the objective weights of each indicator are calculated using the CRITIC method.

5. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The process involves constructing a comprehensive cloud model of gas explosion risk based on the numerical characteristics of various indicators of the coal mine to be evaluated and the combined weights of these indicators. This includes: Each indicator of the coal mine to be evaluated is scored independently; Calculate the numerical characteristics of each indicator based on the scoring data; Based on the numerical characteristics and combined weights of each indicator, the numerical characteristics are aggregated layer by layer to the corresponding criteria layer; wherein, the criteria layer includes several categories, and multiple indicators affect several of the categories respectively; Through comprehensive calculations, the digital characteristics of the comprehensive cloud model of gas explosion risk are obtained, in order to construct the comprehensive cloud model.

6. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The method further includes: The similarity between the integrated cloud model and multiple standard clouds is calculated respectively, and the standard cloud corresponding to the largest calculation result is taken as the standard cloud that is most similar to the integrated cloud model.

7. The gas explosion risk assessment method based on combined weighting according to claim 6, characterized in that, The formula for calculating the closeness is: ,in, The expected risk probability of the integrated cloud model. The expected level of danger for the integrated cloud model; The expected risk probability of the standard cloud. The expected level of danger for the standard cloud.

8. The gas explosion risk assessment method based on combined weighting according to claim 1, characterized in that, The method further includes: The cloud maps of each of the standard clouds and the cloud map of the integrated cloud model are drawn respectively. The cloud map of the integrated cloud model is compared with the cloud maps of each of the standard clouds. The cloud map of the standard cloud that is closest to the cloud map of the integrated cloud model is selected as the cloud map of the standard cloud that is most similar to the integrated cloud model.

9. The gas explosion risk assessment method based on combined weighting according to any one of claims 1-8, characterized in that, The various risk levels mentioned include low risk, lower risk, medium risk, higher risk, and high risk.

10. The gas explosion risk assessment method based on combined weighting according to claim 9, characterized in that, The cloud droplet value of the standard cloud corresponding to the low risk ranges from 0 to 1.6, with an expected value of 0.8, an entropy of 0.27, and a hyperentropy of 0.

03. The cloud droplet value of the standard cloud corresponding to the lower risk ranges from 1.6 to 4.8, with an expected value of 3.2, an entropy of 0.53, and a hyperentropy of 0.

05. The cloud droplet value of the standard cloud corresponding to the medium risk ranges from 4.8 to 7.6, with an expected value of 6.2, an entropy of 0.70, and a hyperentropy of 0.

07. The cloud droplet value of the standard cloud corresponding to the higher risk ranges from 7.6 to 8.8, with an expected value of 8.2, an entropy of 0.20, and a hyperentropy of 0.

02. The cloud droplet value of the standard cloud corresponding to the high risk ranges from 8.8 to 10, with an expected value of 9.4, an entropy of 0.20, and a hyperentropy of 0.02.

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