Method for judging hazard grade of harmful gas in hydraulic tunnel

Abstract

The invention discloses a hydraulic tunnel harmful gas hazard grade determination method comprising the following steps: S1, determining the high gas grade lower limit value of an extra-small section tunnel based on the specifications of the underground engineering field and the characteristics of a hydraulic tunnel; s2, calculating the absolute emission amount in the tunnel by adopting a refined wind measurement method aiming at the characteristics of the hydraulic tunnel; s3, aiming at the ventilation characteristics of the hydraulic tunnel, constructing a minimum ventilation demand calculation model based on the section size and the gas emission amount; s4, constructing a damage grade judgment matrix based on the section size, and associating the tunnel section classification with the gas emission amount data; s5, aiming at the non-uniform gas emission characteristic of the hydraulic tunnel, constructing a self-adaptive parameter regulation and control model; s6, in the high-risk gas area, verifying the reliability of hazard grade judgment by using the multivariable correction model; and S7, designing a construction ventilation scheme and safety management and control measures according to a judgment result. The method has the advantages of being high in applicability, high in real-time performance and high in safety guarantee capability.

Description

Technical Field

[0001] The present invention relates to the technical field of harmful gas level determination, and in particular to a method for determining the hazard level of harmful gas in a hydraulic tunnel. Background Art

[0002] With the continuous development of water conservancy projects and underground tunnel construction in my country, hydraulic tunnels play an important role in infrastructure construction. However, due to the complexity of the underground environment, the problem of harmful gases in hydraulic tunnels has gradually emerged, especially in high-gas areas. The fluctuation of gas concentration and the unevenness of gas outflow have brought great challenges to construction and operation. Accurate determination of the hazard level of harmful gases has become a core issue to ensure the safety of hydraulic tunnel construction and operation.

[0003] In the existing technology, the management of harmful gases in tunnels mostly relies on relevant standards of the coal mining industry or the experience of highway and railway gas tunnels. These methods have limitations in the specific application of hydraulic tunnels, which are mainly reflected in the following aspects:

[0004] 1. Lack of applicability to the characteristics of hydraulic tunnels: The existing methods for determining gas hazard levels are mostly based on the specifications of coal mines or traffic tunnels, and do not fully consider the cross-sectional size characteristics, construction characteristics and environmental differences of hydraulic tunnels. In particular, for the hazard level of ultra-small cross-section tunnels, the existing technology has failed to provide a scientific and reasonable determination standard, resulting in poor applicability of risk assessment results.

[0005] 2. Insufficient dynamic monitoring and evaluation capabilities: Traditional gas monitoring methods rely on static standards or rules and fail to achieve dynamic monitoring and real-time adjustment of gas concentration and outflow volume. This method lacks dynamic adaptability and is difficult to cope with the actual needs of rapid changes in gas concentration during hydraulic tunnel construction.

[0006] 3. Insufficient optimization of ventilation design: Existing technologies mostly use empirical formulas to calculate the minimum ventilation volume, and fail to combine the gas outburst characteristics and regional differences for refined design. The ventilation equipment layout and air volume distribution strategy lack scientific basis and cannot effectively deal with the uneven distribution and dynamic changes of gas concentration.

[0007] 4. Lack of industry norms and graded management standards: Exclusive industry technical specifications for hazardous gas hazard levels have not yet been established in the field of hydraulic tunnels. Construction risk management mostly relies on general standards, and a graded construction and operation management strategy has not been formulated for tunnels of different hazard levels, posing a major safety hazard.

[0008] Therefore, how to provide a method for determining the hazard level of harmful gases in hydraulic tunnels is an urgent problem that technicians in this field need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a method and standard for determining the hazard level of harmful gases in hydraulic tunnels. The present invention constructs a dynamic hazard level determination system based on cross-sectional dimensions and outburst characteristics by introducing methods such as dynamic monitoring of gas outburst, adaptive parameter control, and a multivariable correction model. The present invention describes in detail the technical solutions for hazard level assessment, ventilation optimization design, and hierarchical management, and has the advantages of strong applicability, high real-time performance, and strong safety assurance capabilities.

[0010] A method for determining the hazard level of harmful gases in a hydraulic tunnel according to an embodiment of the present invention comprises the following steps:

[0011] S1. Based on the specifications of underground engineering and the characteristics of hydraulic tunnels, establish the hazard classification standards for tunnels of different cross-section sizes, and clarify the lower limit of high gas level for tunnels with very small cross-sections;

[0012] S2. Adopt a refined wind measurement method tailored to the characteristics of hydraulic tunnels, combine real-time wind speed and gas concentration data, and calculate the absolute outflow volume in the tunnel, so that the calculation results are consistent with the actual construction environment;

[0013] S3. According to the ventilation characteristics of hydraulic tunnels, a minimum ventilation demand calculation model based on cross-sectional dimensions and gas outflow is constructed to calculate the minimum ventilation requirements and optimize the dynamic balance between air volume and gas concentration control;

[0014] S4. Construct a hazard level determination matrix based on section size, associate tunnel section classification with gas emission data, and generate accurate hazard level mapping for different section categories;

[0015] S5. Aiming at the uneven gas outburst characteristics of hydraulic tunnels, an adaptive parameter control model is constructed to dynamically adjust the hazard level threshold by real-time monitoring of gas concentration and air volume;

[0016] S6. In high-risk gas areas, the reliability of hazard level determination is verified using a multivariate calibration model;

[0017] S7. Formulate industry technical specifications for the hazard levels of harmful gases in hydraulic tunnels, design construction ventilation plans and safety control measures based on the determination results, and support risk control and operation management of different types of tunnels through a graded management strategy.

[0018] Optionally, the S1 specifically includes:

[0019] S11. Based on the underground engineering industry specifications and combined with the characteristics of hydraulic tunnels, develop hierarchical hazard level determination standards for different cross-sectional sizes, and introduce a dynamic correlation model between cross-sectional size and gas hazard level;

[0020] S12. For the ultra-small cross-section tunnels, a quantitative determination method for the lower limit of high gas level is proposed, and the lower limit value is clearly quantified to determine the lower limit standard of gas hazard level for ultra-small cross-section tunnels:

[0021]

[0022] Among them, Q g is the absolute gas emission of the extra-small cross-section tunnel, K is the gas emission imbalance coefficient, ranging from 1.6 to 2.0, dynamically adjusted, A 1 The actual area of ​​the tunnel excavation section is optimized by combining the characteristics of the extra-small section. max is the maximum allowable concentration of gas in the tunnel, which is coupled with the construction environment in the calculation. in is the gas concentration in the incoming air, obtained based on real-time monitoring, and ΔT is the hazard level calculation period, which dynamically adapts to the actual construction conditions;

[0023] S13. In the classification of gas hazard levels, the critical conditions of very small cross-section tunnels are refined into independent classification standards:

[0024] Hazard level = f(A 1 , Q g , C max );

[0025] The function f is based on dynamic sampling and calculation feedback and is used to generate judgment parameters for very small sections;

[0026] S14. Conduct high-precision simulation of gas hazard level standards for tunnels of different cross-sectional sizes, and verify the applicability of the lower limit value under extreme conditions by adjusting the judgment standard value in real time;

[0027] S15. Build a database based on the hazard level standards for ultra-small section tunnels, and combine it with the actual data of hydraulic tunnels to form a multivariate association model.

[0028] Optionally, the S2 specifically includes:

[0029] S21. Based on the environmental characteristics of hydraulic tunnels, a refined wind measurement method is adopted, and wind speed and gas concentration sensors are arranged in combination with the dynamic flow field characteristics of the tunnel section to form a distributed real-time monitoring system;

[0030] S22. Perform multivariable dynamic correction on the wind measurement section and calculate the ventilation volume:

[0031] Q v =v·A 2 ·K c ;

[0032] Among them, Q v is the ventilation volume of the wind measuring section, v is the average wind speed actually measured at the wind measuring section, A2 is the effective ventilation area of ​​the wind measurement section, K c It is the tunnel wind flow correction coefficient, which is dynamically adjusted according to the cross-sectional shape and construction disturbance factors;

[0033] S23. Calculate the absolute gas outflow volume based on real-time gas concentration monitoring data and dynamic air volume:

[0034] Q g =Q v ·C g ·K d ;

[0035] Among them, Q g is the absolute gas outflow, Q v is the ventilation volume of the wind section, C g is the real-time gas concentration of the wind section, expressed as a percentage, K d It is the dynamic adjustment coefficient of the outflow volume, which is used to correct the nonlinear coupling between the gas concentration and the ventilation volume change;

[0036] S24. In view of the non-uniformity of gas concentration distribution in the tunnel, a gas concentration distribution model is generated by combining multi-point sensor data to perform gas concentration averaging processing:

[0037]

[0038] in, is the mean gas concentration, C gi is the gas concentration at the ith monitoring point, w i is the weight of the corresponding monitoring point, n is the number of monitoring points;

[0039] S25. Aiming at the dynamic deviation between the absolute gas emission and the actual construction environment, a gas emission prediction model based on long short-term memory network is constructed to dynamically correct the wind measurement results through real-time monitoring data;

[0040] S26. Establish an intelligent feedback mechanism for gas outburst monitoring, combine real-time monitoring with prediction models, and dynamically adjust wind measurement parameters and sensor layout strategies.

[0041] Optionally, the S3 specifically includes:

[0042] S31. Based on the cross-sectional dimensions of hydraulic tunnels and gas outburst characteristics, the minimum safe air volume Q is defined in combination with the dynamic characteristics of gas outburst. min :

[0043]

[0044] Among them, Q min is the minimum ventilation volume of the tunnel, Q g is the absolute gas outflow, Cmax is the maximum allowable concentration of gas, expressed as a percentage, C in is the gas concentration in the incoming air, expressed as a percentage, K b is the correction factor based on the characteristics of the tunnel section and construction stage;

[0045] S32. Build a dynamic correlation model between section size, outflow and air volume demand, and optimize ventilation volume:

[0046]

[0047] Among them, Q opt is the optimized ventilation volume, W var is the temporal and spatial variation factor of gas emission, K c is the dynamic balance coefficient;

[0048] S33. Aiming at the non-uniform distribution of gas concentration, an air volume allocation priority model is constructed to concentrate ventilation resources in areas with high gas concentration and optimize the air volume configuration strategy;

[0049] S34. Use regional dynamic weighting mechanism to calculate ventilation demand regionally:

[0050]

[0051] Among them, Q region is the air volume distribution of the ith area, W i is the gas outburst weight of the ith region, W j is the gas emission weight of the jth area, indicating the contribution ratio of the gas emission of all monitoring areas to the total amount, and n is the total number of monitoring areas;

[0052] S35. Establish a dynamic control algorithm based on optimal air volume distribution, and update the air volume model parameters online through real-time monitoring data to achieve a dynamic balance between ventilation demand and gas concentration control;

[0053] S36. Build an intelligent ventilation management system, combine the characteristics of the tunnel construction stage and the law of gas outburst, and adjust the air volume distribution and minimum ventilation requirements in real time.

[0054] Optionally, the S4 specifically includes:

[0055] S41. Based on the characteristics of the cross-sectional dimensions of hydraulic tunnels, a dynamic cross-sectional classification model is constructed to divide the tunnel cross-sections into extra-small cross-sections, small cross-sections, medium cross-sections, large cross-sections and extra-large cross-sections, and key judgment parameters related to gas emission are defined;

[0056] S42. A gas hazard level determination matrix M is proposed to describe the mapping between the cross-section size category and the hazard level of the gas outburst volume:

[0057]

[0058] Among them, H ij It represents the hazard level value corresponding to the cross-section size category i and the gas emission level j, m and n are the number of cross-section categories and the number of gas levels respectively;

[0059] S43, the real-time monitored gas outflow data Q g The tunnel section category D is input into the dynamic judgment function f to calculate the current hazard level H:

[0060] H=f(D,Q g , T i );

[0061] Among them, T i is the dynamic adjustment threshold of section category i, which is updated in real time according to the progress of tunnel construction;

[0062] S44. Based on the hazard level mapping results, a regional dynamic optimization algorithm is used to calculate the regional hazard level weight W region :

[0063]

[0064] Among them, H i and Q gi are the hazard level and gas outburst volume of the ith area, H k is the hazard level value of the kth area, Q gk is the absolute gas outflow of the kth area;

[0065] S45. Establish a multi-dimensional hazard level verification mechanism to automatically correct the judgment results through dynamic cross-validation of section size, gas outburst volume and construction conditions;

[0066] S46. Build an intelligent mapping database based on section dimensions and gas hazard levels, associate hazard levels, section classifications, and gas outburst characteristic data, and generate a standardized reference model for construction and operation management.

[0067] Optionally, the S5 specifically includes:

[0068] S51. Based on the uneven characteristics of gas outflow, a dynamic outflow control coefficient K is adopted. uneq :

[0069]

[0070] Among them, C gi is the gas concentration at the ith monitoring point, is the mean gas concentration, n is the number of monitoring points;

[0071] S52, build an adaptive parameter control model, monitor gas concentration and air volume data in real time, and dynamically update the hazard level threshold H new :

[0072]

[0073] Among them, H base is the initial hazard level threshold, K adaotive It is an adaptive control coefficient to balance the influence of gas outburst unevenness on the threshold;

[0074] S53. According to the characteristics of gas concentration changes in different tunnel section sizes and construction stages, a dynamic hazard level determination function P is established. dyn , judge the joint impact of hazard level according to section size, gas concentration and outflow volume:

[0075] P dyn =f(D,Q g , K uneq );

[0076] Among them, D is the cross-sectional size category, Q g is the gas outflow volume;

[0077] S54, introduce a timing adjustment mechanism, combine monitoring data and historical trends of hazard levels, optimize real-time judgment results, and predict the hazard level threshold of the next time step:

[0078] H pred =H new +α·ΔH;

[0079] Among them, H pred is the predicted hazard level threshold, ΔH is the level change in the previous time step, and α is the adjustment factor;

[0080] S55. Construct a feedback optimization mechanism to correct the key parameters of the control model in real time, combine the monitoring data with the prediction results, and generate an adaptive judgment optimization strategy;

[0081] S56. Construct an adaptive parameter control database to store the outflow data, concentration distribution characteristics and hazard level adjustment results in the form of time series.

[0082] Optionally, the S6 specifically includes:

[0083] S61. Aiming at the complex environmental conditions in high-risk gas areas, a multivariate correction model is constructed to comprehensively reflect the changing characteristics of gas hazard levels;

[0084] S62. Based on multivariate data, real-time monitoring data is combined with the judgment model to dynamically modify the hazard level:

[0085]

[0086] Among them, H corr is the corrected hazard level, H init is the initial hazard level, W i is the dynamic weight of the i-th variable, ΔX i is the real-time deviation value of the variable, and λ is the model adjustment factor;

[0087] S63. Use the dynamic weight adjustment mechanism to incorporate the coupling characteristics of gas concentration, outflow and other variables into the model, and adjust the weight factor W. i Realize real-time adjustments;

[0088] S64. Combine historical trend data with current monitoring results to predict the change in hazard level in the next time step:

[0089] H pred =H corr +α·ΔH trend ;

[0090] Among them, H pred is the predicted hazard level, α is the trend adjustment factor, ΔH trend The changing value indicating the historical trend of the hazard level;

[0091] S65. Through real-time calibration and iterative optimization, compare the hazard level after calibration with the actual value calculated from the real-time monitoring data, evaluate the reliability of the calibration model, and adjust the model parameters according to the difference;

[0092] S66. Construct a multivariable correction database for high-risk gas areas, store real-time monitoring data, correction model parameters, and hazard level change records in the form of time series, and conduct multi-dimensional verification of the correction results to make the hazard level determination results applicable under different environmental conditions. Through normalized analysis of data over multiple time periods, a correction feedback mechanism for different gas outburst characteristics is formed.

[0093] Optionally, the S7 specifically includes:

[0094] S71. Based on the results of the determination of the hazard level of harmful gases, formulate ventilation design specifications for tunnel construction, including minimum ventilation volume, ventilation equipment layout and air volume distribution strategy;

[0095] S72. Design safety control measures for high-risk gas areas, including the layout of the real-time monitoring system, the configuration of escape routes, and emergency response plans, and establish a dynamic adjustment mechanism to adapt to real-time changes in gas concentration and outflow volume;

[0096] S73. Establish a risk classification management system for tunnel construction. According to the characteristics of tunnels with different hazard levels, formulate graded construction management standards, classify tunnels into high-risk, medium-high-risk and low-risk levels, and provide corresponding control measures for each level;

[0097] S74. Optimize the dynamic parameter adjustment model of the ventilation scheme based on the determination results and calculate the ventilation demand of the partition:

[0098]

[0099] Among them, Q zone is the ventilation volume of a specific area, Q total is the total ventilation volume of the tunnel, W zone is the regional gas outburst weight, n is the total number of regions, W i is the gas emission weight of the ith region, indicating the proportion of the gas emission in the region in the total emission;

[0100] S75. Develop operational management specifications for high-risk gas areas, including real-time monitoring, periodic testing and dynamic adjustment processes, and update the hazard level determination results of tunnels through risk assessment.

[0101] The beneficial effects of the present invention are:

[0102] (1) The present invention provides a scientific assessment and dynamic adaptation of the hazard level of harmful gases in hydraulic tunnels by combining dynamic monitoring of gas outburst, adaptive parameter control, multivariable correction model and hazard level mapping matrix, so that the system can respond to the complex and changeable tunnel environment in real time, especially in high-gas areas, and can accurately determine the hazard level, optimize ventilation design and improve safety management and control capabilities.

[0103] (2) The present invention realizes dynamic risk assessment and safety management of tunnel construction by combining a real-time monitoring system, an adaptive parameter control model and a hierarchical management strategy. Real-time adjustment of the hazard level threshold and optimization of the ventilation strategy not only improves the accuracy of hazard level determination, but also significantly improves the response speed to risks and the safety of the construction process.

[0104] (3) The present invention provides a scientific basis and dynamic adjustment capability for tunnel construction and operation by establishing industry technical specifications and data storage and analysis systems for gas hazard levels. It can systematically solve the problem of the lack of hazard level determination standards for hydraulic tunnel characteristics in the prior art, thereby achieving refined management of the entire process from construction to operation and improving tunnel construction efficiency and operational safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0106] Figure 1 This is a general framework diagram of a method and standard for determining the hazard level of harmful gases in hydraulic tunnels proposed by the present invention;

[0107] Figure 2 This is a flow chart of a method for determining the hazard level of harmful gases in a hydraulic tunnel proposed by the present invention;

[0108] Figure 3 The present invention provides a structural schematic diagram of a method for determining the hazard level of harmful gases in a hydraulic tunnel and a standard dynamic gas outburst monitoring and hazard level determination system. DETAILED DESCRIPTION

[0109] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0110] refer to Figure 1-3 A method for determining the hazard level of harmful gases in a hydraulic tunnel comprises the following steps:

[0111] S1. Based on the specifications of underground engineering and the characteristics of hydraulic tunnels, establish the hazard classification standards for tunnels of different cross-section sizes, and clarify the lower limit of high gas level for tunnels with very small cross-sections;

[0112] In this implementation, S1 specifically includes:

[0113] S11. Based on the underground engineering industry specifications and combined with the characteristics of hydraulic tunnels, develop hierarchical hazard level determination standards for different cross-sectional sizes, and introduce a dynamic correlation model between cross-sectional size and gas hazard level;

[0114] S12. For the ultra-small cross-section tunnels, a quantitative determination method for the lower limit of high gas level is proposed, and the lower limit value is clearly quantified to determine the lower limit standard of gas hazard level for ultra-small cross-section tunnels:

[0115]

[0116] Among them, Q g is the absolute gas emission of the extra-small cross-section tunnel, K is the gas emission imbalance coefficient, ranging from 1.6 to 2.0, dynamically adjusted, A 1 The actual area of ​​the tunnel excavation section is optimized by combining the characteristics of the extra-small section. max is the maximum allowable concentration of gas in the tunnel, which is coupled with the construction environment in the calculation. inis the gas concentration in the incoming air, obtained based on real-time monitoring, and ΔT is the hazard level calculation period, which dynamically adapts to the actual construction conditions;

[0117] S13. In the classification of gas hazard levels, the critical conditions of very small cross-section tunnels are refined into independent classification standards:

[0118] Hazard level = f(A 1 , Q g , C max );

[0119] The function f is based on dynamic sampling and calculation feedback and is used to generate judgment parameters for very small sections;

[0120] S14. Conduct high-precision simulation of gas hazard level standards for tunnels of different cross-sectional sizes, and verify the applicability of the lower limit value under extreme conditions by adjusting the judgment standard value in real time;

[0121] S15. Build a database based on the hazard level standards for ultra-small section tunnels, and combine it with the actual data of hydraulic tunnels to form a multivariate association model.

[0122] This implementation method constructs an adaptive parameter control model under the characteristics of uneven gas emission, dynamically adjusts the gas hazard level threshold, and combines real-time monitoring data and historical trend changes to accurately identify the fluctuation characteristics of gas concentration in different areas, which significantly improves the accuracy of hazard level judgment and response efficiency, and provides strong technical support for dynamic risk management of hydraulic tunnels.

[0123] S2. Adopt a refined wind measurement method tailored to the characteristics of hydraulic tunnels, combine real-time wind speed and gas concentration data, and calculate the absolute outflow volume in the tunnel, so that the calculation results are consistent with the actual construction environment;

[0124] In this implementation, S2 specifically includes:

[0125] S21. Based on the environmental characteristics of hydraulic tunnels, a refined wind measurement method is adopted, and wind speed and gas concentration sensors are arranged in combination with the dynamic flow field characteristics of the tunnel section to form a distributed real-time monitoring system;

[0126] S22. Perform multivariable dynamic correction on the wind measurement section and calculate the ventilation volume:

[0127] Q v =v·A 2 ·K c ;

[0128] Among them, Q v is the ventilation volume of the wind measuring section, v is the average wind speed actually measured at the wind measuring section, A 2 is the effective ventilation area of ​​the wind measurement section, K cIt is the tunnel wind flow correction coefficient, which is dynamically adjusted according to the cross-sectional shape and construction disturbance factors;

[0129] S23. Calculate the absolute gas outflow volume based on real-time gas concentration monitoring data and dynamic air volume:

[0130] Q g =Q v ·C g ·K d ;

[0131] Among them, Q g is the absolute gas outflow, Q v is the ventilation volume of the wind section, C g is the real-time gas concentration of the wind section, expressed as a percentage, K d It is the dynamic adjustment coefficient of the outflow volume, which is used to correct the nonlinear coupling between the gas concentration and the ventilation volume change;

[0132] S24. In view of the non-uniformity of gas concentration distribution in the tunnel, a gas concentration distribution model is generated by combining multi-point sensor data to perform gas concentration averaging processing:

[0133]

[0134] in, is the mean gas concentration, C gi is the gas concentration at the ith monitoring point, w i is the weight of the corresponding monitoring point, n is the number of monitoring points;

[0135] S25. Aiming at the dynamic deviation between the absolute gas emission and the actual construction environment, a gas emission prediction model based on long short-term memory network is constructed to dynamically correct the wind measurement results through real-time monitoring data;

[0136] S26. Establish an intelligent feedback mechanism for gas outburst monitoring, combine real-time monitoring with prediction models, and dynamically adjust wind measurement parameters and sensor layout strategies.

[0137] This implementation method constructs a minimum ventilation demand calculation model based on cross-sectional dimensions and gas outburst volume, and optimizes ventilation design in combination with dynamic correlation parameters, thereby achieving a precise match between gas hazard level determination and ventilation strategy adjustment. This not only improves the efficiency of gas concentration control, but also significantly enhances the safety of tunnel construction and operation, providing a scientific basis for risk management of tunnels of different cross-sectional types.

[0138] S3. According to the ventilation characteristics of hydraulic tunnels, a minimum ventilation demand calculation model based on cross-sectional dimensions and gas outflow is constructed to calculate the minimum ventilation requirements and optimize the dynamic balance between air volume and gas concentration control;

[0139] In this implementation, S3 specifically includes:

[0140] S31. Based on the cross-sectional dimensions of hydraulic tunnels and gas outburst characteristics, the minimum safe air volume Q is defined in combination with the dynamic characteristics of gas outburst. min :

[0141]

[0142] Among them, Q min is the minimum ventilation volume of the tunnel, Q g is the absolute gas outflow, C max is the maximum allowable concentration of gas, expressed as a percentage, C in is the gas concentration in the incoming air, expressed as a percentage, K b is the correction factor based on the characteristics of the tunnel section and construction stage;

[0143] S32. Build a dynamic correlation model between section size, outflow and air volume demand, and optimize ventilation volume:

[0144]

[0145] Among them, Q opt is the optimized ventilation volume, W var is the temporal and spatial variation factor of gas outflow, Kc is the dynamic balance coefficient;

[0146] S33. Aiming at the non-uniform distribution of gas concentration, an air volume allocation priority model is constructed to concentrate ventilation resources in areas with high gas concentration and optimize the air volume configuration strategy;

[0147] S34. Use regional dynamic weighting mechanism to calculate ventilation demand regionally:

[0148]

[0149] Among them, Q region is the air volume distribution of the ith area, W i is the gas outburst weight of the area, W j is the gas emission weight of the jth area, indicating the contribution ratio of the gas emission of all monitoring areas to the total amount, and n is the total number of monitoring areas;

[0150] S35. Establish a dynamic control algorithm based on optimal air volume distribution, and update the air volume model parameters online through real-time monitoring data to achieve a dynamic balance between ventilation demand and gas concentration control;

[0151] S36. Build an intelligent ventilation management system, combine the characteristics of the tunnel construction stage and the law of gas outburst, and adjust the air volume distribution and minimum ventilation requirements in real time.

[0152] This implementation method combines the cross-sectional dimensions, gas outflow and ventilation requirements to build a dynamic correlation model, optimize the ventilation distribution strategy, and ensure the accuracy and dynamic adaptability of gas concentration control. The ventilation optimization scheme not only improves ventilation efficiency, but also significantly reduces the risk of excessive gas concentration, providing a scientific basis and efficient safety guarantee for the construction and operation of hydraulic tunnels.

[0153] S4. Construct a hazard level determination matrix based on section size, associate tunnel section classification with gas emission data, and generate accurate hazard level mapping for different section categories;

[0154] In this implementation, S4 specifically includes:

[0155] S41. Based on the characteristics of the cross-sectional dimensions of hydraulic tunnels, a dynamic cross-sectional classification model is constructed to divide the tunnel cross-sections into extra-small cross-sections, small cross-sections, medium cross-sections, large cross-sections and extra-large cross-sections, and key judgment parameters related to gas emission are defined;

[0156] S42. A gas hazard level determination matrix M is proposed to describe the mapping between the cross-section size category and the hazard level of the gas outburst volume:

[0157]

[0158] Among them, H ij It represents the hazard level value corresponding to the cross-section size category i and the gas emission level j, m and n are the number of cross-section categories and the number of gas levels respectively;

[0159] S43, the real-time monitored gas outflow data Q g The tunnel section category D is input into the dynamic judgment function f to calculate the current hazard level H:

[0160] H=f(D,Q g , T i );

[0161] Among them, Ti is the dynamic adjustment threshold of section category i, which is updated in real time according to the progress of tunnel construction;

[0162] S44. Based on the hazard level mapping results, a regional dynamic optimization algorithm is used to calculate the regional hazard level weight W region :

[0163]

[0164] Among them, H i and Q gi are the hazard level and gas outburst volume of the ith area, H k is the hazard level value of the kth area, Q gkis the absolute gas outflow of the kth area;

[0165] S45. Establish a multi-dimensional hazard level verification mechanism to automatically correct the judgment results through dynamic cross-validation of section size, gas outburst volume and construction conditions;

[0166] S46. Build an intelligent mapping database based on section dimensions and gas hazard levels, associate hazard levels, section classifications, and gas outburst characteristic data, and generate a standardized reference model for construction and operation management.

[0167] This implementation method achieves accurate determination of the gas hazard level of hydraulic tunnels by constructing a hazard level determination matrix based on cross-sectional dimensions and gas outflow volume, combined with dynamic monitoring and classification standards. Matrix analysis not only improves the scientific nature of the assessment, but also optimizes resource allocation, making ventilation design and safety management more targeted and efficient, and providing comprehensive support for construction and operation.

[0168] S5. Aiming at the uneven gas outburst characteristics of hydraulic tunnels, an adaptive parameter control model is constructed to dynamically adjust the hazard level threshold by real-time monitoring of gas concentration and air volume;

[0169] In this implementation, S5 specifically includes:

[0170] S51. Based on the uneven characteristics of gas outflow, a dynamic outflow control coefficient K is adopted. uneq :

[0171]

[0172] Among them, C gi is the gas concentration at the ith monitoring point, is the mean gas concentration, n is the number of monitoring points;

[0173] S52, build an adaptive parameter control model, monitor gas concentration and air volume data in real time, and dynamically update the hazard level threshold H new :

[0174]

[0175] Among them, H base is the initial hazard level threshold, K adaptive It is an adaptive control coefficient to balance the influence of gas outburst unevenness on the threshold;

[0176] S53. According to the characteristics of gas concentration changes in different tunnel section sizes and construction stages, a dynamic hazard level determination function P is established. dyn , judge the joint impact of hazard level according to section size, gas concentration and outflow volume:

[0177] P dyn =f(D,Q g , K uneq );

[0178] Among them, D is the cross-sectional size category, Q g is the gas outflow volume;

[0179] S54, introduce a timing adjustment mechanism, combine monitoring data and historical trends of hazard levels, optimize real-time judgment results, and predict the hazard level threshold of the next time step:

[0180] H pred =H new +α·ΔH;

[0181] Among them, H pred is the predicted hazard level threshold, ΔH is the level change in the previous time step, and α is the adjustment factor;

[0182] S55. Construct a feedback optimization mechanism to correct the key parameters of the control model in real time, combine the monitoring data with the prediction results, and generate an adaptive judgment optimization strategy;

[0183] S56. Construct an adaptive parameter control database to store the outflow data, concentration distribution characteristics and hazard level adjustment results in the form of time series.

[0184] This implementation method realizes dynamic adjustment of the unevenness of gas concentration and outflow volume by combining the dynamic outflow volume control coefficient with the adaptive parameter control model, accurately optimizes the real-time calculation and determination process of the hazard level threshold, effectively improves the accuracy and response efficiency of the hazard level assessment, and provides a scientific risk management tool for tunnel construction and operation.

[0185] S6. In high-risk gas areas, the reliability of hazard level determination is verified using a multivariate calibration model;

[0186] In this implementation, S6 specifically includes:

[0187] S61. Aiming at the complex environmental conditions in high-risk gas areas, a multivariate correction model is constructed to comprehensively reflect the changing characteristics of gas hazard levels;

[0188] S62. Based on multivariate data, real-time monitoring data is combined with the judgment model to dynamically modify the hazard level:

[0189]

[0190] Among them, H corr is the corrected hazard level, H init is the initial hazard level, W iis the dynamic weight of the i-th variable, ΔX i is the real-time deviation value of the variable, and λ is the model adjustment factor;

[0191] S63. Use the dynamic weight adjustment mechanism to incorporate the coupling characteristics of gas concentration, outflow and other variables into the model, and adjust the weight factor W. i Realize real-time adjustments;

[0192] S64. Combine historical trend data with current monitoring results to predict the change in hazard level in the next time step:

[0193] H pred =H corr +α·ΔH trend ;

[0194] Among them, H pred is the predicted hazard level, α is the trend adjustment factor, ΔH trend The changing value indicating the historical trend of the hazard level;

[0195] S65. Through real-time calibration and iterative optimization, compare the hazard level after calibration with the actual value calculated from the real-time monitoring data, evaluate the reliability of the calibration model, and adjust the model parameters according to the difference;

[0196] S66. Construct a multivariable correction database for high-risk gas areas, store real-time monitoring data, correction model parameters, and hazard level change records in the form of time series, and conduct multi-dimensional verification of the correction results to make the hazard level determination results applicable under different environmental conditions. Through normalized analysis of data over multiple time periods, a correction feedback mechanism for different gas outburst characteristics is formed.

[0197] This implementation method constructs a multivariable correction model to comprehensively analyze dynamic monitoring data such as gas concentration, outflow volume, wind speed, etc., and corrects the hazard level determination results in real time. At the same time, it combines historical trends to predict the next level change, which significantly improves the accuracy and real-time performance of hazard level assessment and provides reliable protection for risk management of tunnel construction and operation.

[0198] S7. Formulate industry technical specifications for the hazard levels of harmful gases in hydraulic tunnels, design construction ventilation plans and safety control measures based on the determination results, and support risk control and operation management of different types of tunnels through a graded management strategy.

[0199] In this implementation, S7 specifically includes:

[0200] S71. Based on the results of the determination of the hazard level of harmful gases, formulate ventilation design specifications for tunnel construction, including minimum ventilation volume, ventilation equipment layout and air volume distribution strategy;

[0201] S72. Design safety control measures for high-risk gas areas, including the layout of the real-time monitoring system, the configuration of escape routes, and emergency response plans, and establish a dynamic adjustment mechanism to adapt to real-time changes in gas concentration and outflow volume;

[0202] S73. Establish a risk classification management system for tunnel construction. According to the characteristics of tunnels with different hazard levels, formulate graded construction management standards, classify tunnels into high-risk, medium-high-risk and low-risk levels, and provide corresponding control measures for each level;

[0203] S74. Optimize the dynamic parameter adjustment model of the ventilation scheme based on the determination results and calculate the ventilation demand of the partition:

[0204]

[0205] Among them, Q zone is the ventilation volume of a specific area, Q total is the total ventilation volume of the tunnel, W zone is the regional gas outburst weight, n is the total number of regions, W i is the gas emission weight of the ith region, indicating the proportion of the gas emission in the region in the total emission;

[0206] S75. Develop operational management specifications for high-risk gas areas, including real-time monitoring, periodic testing and dynamic adjustment processes, and update the hazard level determination results of tunnels through risk assessment.

[0207] This implementation method optimizes ventilation design, formulates safety control measures and hierarchical management strategies based on the determination results of gas hazard levels, and combines dynamic parameter adjustment models and risk assessment processes. It not only achieves refined management of high-risk gas areas, but also effectively improves the safety and efficiency of tunnel construction and operation, providing a systematic safety assurance system for hydraulic tunnels.

[0208] Embodiment 1:

[0209] In order to verify the feasibility and practical application effect of the present invention, the present invention is applied to the construction and operation management of a large-scale hydraulic tunnel project in Sichuan Province. The project includes multiple main tunnels, and the tunnel section types range from extra-small sections to extra-large sections. The construction environment is complex. Two of the tunnels are located in high-gas areas, and the gas concentration fluctuates greatly and has obvious non-uniformity. In the early stages of construction, due to the lack of systematic harmful gas hazard level determination standards and dynamic risk assessment capabilities, gas concentrations frequently exceeded the standard, resulting in multiple construction interruptions, which seriously affected the progress and safety of the project. In order to solve the above problems, the construction unit fully deployed the method and standard for determining the harmful gas hazard level of the hydraulic tunnel proposed in the present invention, and verified its effect in different scenarios.

[0210] During the construction phase, the present invention collects and analyzes key data such as real-time gas concentration, outflow volume, ventilation volume, etc. in the tunnel through a gas outflow dynamic monitoring system, and constructs a dynamic hazard level determination matrix in combination with the tunnel section size and environmental characteristics. For example, in a cross-sectional tunnel, the monitoring system collected gas concentrations ranging from 0.18% to 0.35%, which exceeded the original set safety threshold of 0.30% many times. Through the method of the present invention, the system analyzes the fluctuations in gas concentration and the changes in outflow volume in real time, and determines the area as a "high-risk area" based on the hazard level determination matrix. Subsequently, the system automatically calculates the minimum ventilation requirement, and the results show that the minimum ventilation volume needs to reach 220m per minute. 3 , while the original ventilation equipment configuration was only 180m 3 / min. After adjusting the ventilation equipment, the gas concentration dropped to 0.28% within 15 minutes, meeting the construction safety requirements.

[0211] In the tunnel operation stage, the present invention uses an adaptive parameter control model and a multivariable correction model to dynamically adjust the gas hazard level. Due to the small cross-section size and concentrated gas outflow in a very small cross-section tunnel, the monitoring system recorded a peak gas outflow of 0.62m 3 / min, while the daily average is only 0.30m 3 / min. The system optimizes the gas concentration safety threshold from the initial setting of 0.32% to 0.29% through a dynamic weight adjustment mechanism, and optimizes the ventilation volume distribution strategy in real time to stabilize the gas concentration below 0.27%, ensuring the long-term safe operation of the tunnel.

[0212] In actual tests, the present invention was used to determine the hazard levels of tunnels with different cross-section types. The specific determination results are shown in Table 1 below:

[0213] Table 1 Determination index table of absolute gas emission in high gas area of ​​tunnel

[0214]

[0215]

[0216] It can be seen from Table 1 that the hazard level of tunnels of different cross-section types is determined according to the absolute gas outburst volume, especially the standard value of the extra-small cross-section tunnel is clearly 0.5m 3 / min or less, providing a scientific basis for subsequent safety management.

[0217] In order to further quantify the beneficial effects of the present invention, the construction unit compared the gas hazard management situation before and after implementation and recorded the relevant data in detail. The results are shown in Table 2 below:

[0218] Table 2 Comparison of gas hazard management effects in hydraulic tunnels

[0219]

[0220] As can be seen from Table 1, after the implementation of the present invention during the construction phase, the number of times the gas concentration exceeded the standard was significantly reduced from 15 times per month to 1 time, and the number of construction interruptions was completely eliminated. By optimizing the ventilation strategy, the time for the gas concentration to reach the standard was shortened from an average of 50 minutes to 12 minutes, and the construction efficiency was improved by 20%. In addition, the accuracy of gas hazard level determination was increased from 78% to 98%, effectively enhancing the risk management and control capabilities during the construction process.

[0221] In a specific case, a large-section tunnel experienced an abnormal rapid increase in gas concentration during construction. Real-time monitoring data showed that the gas concentration rose from 0.22% to 0.36% within 20 minutes, exceeding the initial safety threshold. The system immediately classified the area as a "secondary high-risk area" through a dynamic judgment model and triggered an automatic ventilation optimization strategy. Through calculation, the system adjusted the ventilation volume to 310m / min. 3 , reducing the gas concentration to 0.28% in just 10 minutes. Subsequent data analysis showed that this rapid response effectively avoided potential safety accidents and ensured the smooth progress of the construction.

[0222] In addition, the present invention also constructs a multivariable correction database for high-risk gas areas during the operation stage, and optimizes the gas concentration control strategy by combining historical data and real-time monitoring information. For example, the gas concentration in a high-gas area fluctuates greatly during multiple construction and operation periods. The monitoring system predicts the trend of gas concentration changes in the next 24 hours through the method of the present invention, and adjusts the ventilation plan in advance, avoiding the problem of continuous high-concentration gas accumulation and ensuring operational safety.

[0223] Through the above embodiments, the present invention solves the problems in the prior art of lack of hazard level determination standards for hydraulic tunnel characteristics, insufficient dynamic monitoring capabilities, and insufficient ventilation design optimization. During the entire construction and operation cycle, the present invention provides a scientific hazard level assessment method, a dynamic risk management and control scheme, and a precise ventilation optimization strategy, which not only significantly improves construction efficiency and safety, but also provides a solid technical guarantee for the long-term stable operation of hydraulic tunnels.

[0224] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for determining the hazard level of harmful gases in hydraulic tunnels, characterized in that: The steps include: S1. Based on the specifications of underground engineering and the characteristics of hydraulic tunnels, establish the hazard classification standards for tunnels of different cross-section sizes, and clarify the lower limit of high gas level for tunnels with very small cross-sections; S2. Adopt a refined wind measurement method tailored to the characteristics of hydraulic tunnels, combine real-time wind speed and gas concentration data, and calculate the absolute outflow volume in the tunnel, so that the calculation results are consistent with the actual construction environment; S3. According to the ventilation characteristics of hydraulic tunnels, a minimum ventilation demand calculation model based on cross-sectional dimensions and gas outflow is constructed to calculate the minimum ventilation requirements and optimize the dynamic balance between air volume and gas concentration control; S4. Construct a hazard level determination matrix based on section size, associate tunnel section classification with gas emission data, and generate accurate hazard level mapping for different section categories; S5. Aiming at the uneven gas outburst characteristics of hydraulic tunnels, an adaptive parameter control model is constructed to dynamically adjust the hazard level threshold by real-time monitoring of gas concentration and air volume; S6. In high-risk gas areas, the reliability of hazard level determination is verified using a multivariate calibration model; S7. Formulate industry technical specifications for the hazard levels of harmful gases in hydraulic tunnels, design construction ventilation plans and safety control measures based on the determination results, and support risk control and operation management of different types of tunnels through a graded management strategy.

2. A method for determining the hazard level of harmful gases in hydraulic tunnels according to claim 1, characterized in that: The S1 specifically includes: S11. Based on the underground engineering industry specifications and combined with the characteristics of hydraulic tunnels, develop hierarchical hazard level determination standards for different cross-sectional sizes, and introduce a dynamic correlation model between cross-sectional size and gas hazard level; S12. For the ultra-small cross-section tunnels, a quantitative determination method for the lower limit of high gas level is proposed, and the lower limit value is clearly quantified to determine the lower limit standard of gas hazard level for ultra-small cross-section tunnels: Among them, Q g is the absolute gas emission of the ultra-small section tunnel, K is the gas emission imbalance coefficient, ranging from 1.6 to 2.0, dynamically adjusted, A1 is the actual area of ​​the tunnel excavation section, combined with the ultra-small section characteristics to optimize the measurement, C max is the maximum allowable concentration of gas in the tunnel, which is coupled with the construction environment in the calculation. in is the gas concentration in the incoming air, obtained based on real-time monitoring, and ΔT is the hazard level calculation period, which dynamically adapts to the actual construction conditions; S13. In the classification of gas hazard levels, the critical conditions of very small cross-section tunnels are refined into independent classification standards: Hazard level = f(A1,Q g ,C max ); The function f is based on dynamic sampling and calculation feedback and is used to generate judgment parameters for very small sections; S14. Conduct high-precision simulation of gas hazard level standards for tunnels of different cross-sectional sizes, and verify the applicability of the lower limit value under extreme conditions by adjusting the judgment standard value in real time; S15. Build a database based on the hazard level standards for ultra-small section tunnels and combine it with the actual data of hydraulic tunnels to form a multivariate association model.

3. A method for determining the hazard level of harmful gases in hydraulic tunnels according to claim 1, characterized in that: The S2 specifically includes: S21. Based on the environmental characteristics of hydraulic tunnels, a refined wind measurement method is adopted, and wind speed and gas concentration sensors are arranged in combination with the dynamic flow field characteristics of the tunnel section to form a distributed real-time monitoring system; S22. Perform multivariable dynamic correction on the wind measurement section and calculate the ventilation volume: Q v =v·A2·K c ; Among them, Q v is the ventilation volume of the wind measuring section, v is the average wind speed actually measured at the wind measuring section, A2 is the effective ventilation area of ​​the wind measuring section, K c It is the tunnel wind flow correction coefficient, which is dynamically adjusted according to the cross-sectional shape and construction disturbance factors; S23. Calculate the absolute gas outflow volume based on real-time gas concentration monitoring data and dynamic air volume: Q g =Q v ·C g ·K d ; Among them, Q g is the absolute gas outflow, Q v is the ventilation volume of the wind section, C g is the real-time gas concentration of the wind section, expressed as a percentage, K d It is the dynamic adjustment coefficient of the outflow volume, which is used to correct the nonlinear coupling between the gas concentration and the ventilation volume change; S24. In view of the non-uniformity of gas concentration distribution in the tunnel, a gas concentration distribution model is generated by combining multi-point sensor data to perform gas concentration averaging processing: in, is the mean gas concentration, C gi is the gas concentration at the ith monitoring point, w i is the weight of the corresponding monitoring point, n is the number of monitoring points; S25. Aiming at the dynamic deviation between the absolute gas emission and the actual construction environment, a gas emission prediction model based on long short-term memory network is constructed to dynamically correct the wind measurement results through real-time monitoring data; S26. Establish an intelligent feedback mechanism for gas outburst monitoring, combine real-time monitoring with prediction models, and dynamically adjust wind measurement parameters and sensor layout strategies.

4. A method for determining the hazard level of harmful gases in a hydraulic tunnel according to claim 1, characterized in that: The S3 specifically includes: S31. Based on the cross-sectional dimensions of hydraulic tunnels and gas outburst characteristics, the minimum safe air volume Q is defined in combination with the dynamic characteristics of gas outburst. min : Among them, Q min is the minimum ventilation volume of the tunnel, Q g is the absolute gas outflow, C max is the maximum allowable concentration of gas, expressed as a percentage, C in is the gas concentration in the incoming air, expressed as a percentage, K b is the correction factor based on the characteristics of the tunnel section and construction stage; S32. Build a dynamic correlation model between section size, outflow and air volume demand, and optimize ventilation volume: Among them, Q opt is the optimized ventilation volume, W var is the temporal and spatial variation factor of gas emission, K c is the dynamic balance coefficient; S33. Aiming at the non-uniform distribution of gas concentration, an air volume allocation priority model is constructed to concentrate ventilation resources in areas with high gas concentration and optimize the air volume configuration strategy; S34. Use regional dynamic weighting mechanism to calculate ventilation demand regionally: Among them, Q region is the air volume distribution of the ith area, W i is the gas outburst weight of the ith region, W j is the gas emission weight of the jth area, indicating the contribution ratio of the gas emission of all monitoring areas to the total amount, and n is the total number of monitoring areas; S35. Establish a dynamic control algorithm based on optimal air volume distribution, and update the air volume model parameters online through real-time monitoring data to achieve a dynamic balance between ventilation demand and gas concentration control; S36. Build an intelligent ventilation management system, combine the characteristics of the tunnel construction stage and the law of gas outburst, and adjust the air volume distribution and minimum ventilation requirements in real time.

5. A method for determining the hazard level of harmful gases in hydraulic tunnels according to claim 1, characterized in that: The S4 specifically includes: S41. Based on the characteristics of the cross-sectional dimensions of hydraulic tunnels, a dynamic cross-sectional classification model is constructed to divide the tunnel cross-sections into extra-small cross-sections, small cross-sections, medium cross-sections, large cross-sections and extra-large cross-sections, and key judgment parameters related to gas emission are defined; S42. A gas hazard level determination matrix M is proposed to describe the mapping between the cross-section size category and the hazard level of the gas outburst volume: Among them, H ij It represents the hazard level value corresponding to the cross-section size category i and the gas emission level j, m and n are the number of cross-section categories and the number of gas levels respectively; S43, the real-time monitored gas outflow data Q g The tunnel section category D is input into the dynamic judgment function f to calculate the current hazard level H: H=f(D,Q g ,T i ); Among them, T i is the dynamic adjustment threshold of section category i, which is updated in real time according to the progress of tunnel construction; S44. Based on the hazard level mapping results, a regional dynamic optimization algorithm is used to calculate the regional hazard level weight W region : Among them, H i and Q gi are the hazard level and gas outburst volume of the ith area, H k is the hazard level value of the kth area, Q gk is the absolute gas outflow of the kth area; S45. Establish a multi-dimensional hazard level verification mechanism to automatically correct the judgment results through dynamic cross-validation of section size, gas outburst volume and construction conditions; S46. Build an intelligent mapping database based on section dimensions and gas hazard levels, associate hazard levels, section classifications, and gas outburst characteristic data, and generate a standardized reference model for construction and operation management.

6. A method for determining the hazard level of harmful gases in hydraulic tunnels according to claim 1, characterized in that: The S5 specifically includes: S51. Based on the uneven characteristics of gas outflow, a dynamic outflow control coefficient K is adopted. uneq : Among them, C gi is the gas concentration at the ith monitoring point, is the mean gas concentration, n is the number of monitoring points; S52, build an adaptive parameter control model, monitor gas concentration and air volume data in real time, and dynamically update the hazard level threshold H new : Among them, H base is the initial hazard level threshold, K adaptive It is an adaptive control coefficient to balance the influence of gas outburst unevenness on the threshold; S53. According to the characteristics of gas concentration changes in different tunnel section sizes and construction stages, a dynamic hazard level determination function P is established. dyn , judge the joint impact of hazard level according to section size, gas concentration and outflow volume: P dyn =f(D,Q g ,K uneq ); Among them, D is the cross-sectional size category, Q g is the gas outflow volume; S54, introduce a timing adjustment mechanism, combine monitoring data and historical trends of hazard levels, optimize real-time judgment results, and predict the hazard level threshold of the next time step: H pred =H new +α·ΔH; Among them, H pred is the predicted hazard level threshold, ΔH is the level change in the previous time step, and α is the adjustment factor; S55. Construct a feedback optimization mechanism to correct the key parameters of the control model in real time, combine the monitoring data with the prediction results, and generate an adaptive judgment optimization strategy; S56. Construct an adaptive parameter control database to store the outflow data, concentration distribution characteristics and hazard level adjustment results in the form of time series.

7. A method for determining the hazard level of harmful gases in hydraulic tunnels according to claim 1, characterized in that: The S6 specifically includes: S61. Aiming at the complex environmental conditions in high-risk gas areas, a multivariate correction model is constructed to comprehensively reflect the changing characteristics of gas hazard levels; S62. Based on multivariate data, real-time monitoring data is combined with the judgment model to dynamically modify the hazard level: Among them, H corr is the corrected hazard level, H init is the initial hazard level, W i is the dynamic weight of the i-th variable, ΔX i is the real-time deviation value of the variable, and λ is the model adjustment factor; S63. Use the dynamic weight adjustment mechanism to incorporate the coupling characteristics of gas concentration, outflow and other variables into the model, and adjust the weight factor W. i Realize real-time adjustments; S64. Combine historical trend data with current monitoring results to predict the change in hazard level in the next time step: H pred =H corr +α·ΔH trend ; Among them, H pred is the predicted hazard level, α is the trend adjustment factor, ΔH trend The changing value indicating the historical trend of the hazard level; S65. Through real-time calibration and iterative optimization, compare the hazard level after calibration with the actual value calculated from the real-time monitoring data, evaluate the reliability of the calibration model, and adjust the model parameters according to the difference; S66. Construct a multivariable correction database for high-risk gas areas, store real-time monitoring data, correction model parameters, and hazard level change records in the form of time series, and conduct multi-dimensional verification of the correction results to make the hazard level determination results applicable under different environmental conditions. Through normalized analysis of data over multiple time periods, a correction feedback mechanism for different gas outburst characteristics is formed.

8. A method for determining the hazard level of harmful gases in a hydraulic tunnel according to claim 1, characterized in that: The S7 specifically includes: S71. Based on the results of the determination of the hazard level of harmful gases, formulate ventilation design specifications for tunnel construction, including minimum ventilation volume, ventilation equipment layout and air volume distribution strategy; S72. Design safety control measures for high-risk gas areas, including the layout of the real-time monitoring system, the configuration of escape routes, and emergency response plans, and establish a dynamic adjustment mechanism to adapt to real-time changes in gas concentration and outflow volume; S73. Establish a risk classification management system for tunnel construction. According to the characteristics of tunnels with different hazard levels, formulate graded construction management standards, classify tunnels into high-risk, medium-high-risk and low-risk levels, and provide corresponding control measures for each level; S74. Optimize the dynamic parameter adjustment model of the ventilation scheme based on the determination results and calculate the ventilation demand of the partition: Among them, Q zone is the ventilation volume of a specific area, Q total is the total ventilation volume of the tunnel, W zone is the regional gas outburst weight, n is the total number of regions, W i is the gas emission weight of the ith region, indicating the proportion of the gas emission in the region in the total emission; S75. Develop operational management specifications for high-risk gas areas, including real-time monitoring, periodic testing and dynamic adjustment processes, and update the hazard level determination results of tunnels through risk assessment.

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