An intelligent control system and method for mine ventilation automation

Abstract

The present invention relates to the technical field of mine ventilation, and particularly to an intelligent control system and method for mine ventilation automation. It includes: preprocessing the original data to obtain the preprocessed data; performing intelligent analysis on the preprocessed data using a multiple adaptive dynamic game optimization algorithm to obtain an analysis result, and generating a final control strategy based on the analysis result; introducing predictive feedback and real-time feedback to dynamically adjust the final control strategy to obtain an adjusted control strategy; based on the adjusted control strategy and the preprocessed data, obtaining an adjusted control instruction and an emergency control instruction to adjust the control equipment in the mine; solving the technical problems of poor self-adaptability and low efficiency of ventilation control in the complex environment of mine ventilation.

Description

Technical Field

[0001] The present invention relates to the technical field of mine ventilation, and particularly to an intelligent control system and method for mine ventilation automation. Background Art

[0002] The mine ventilation system is one of the core facilities to ensure the safety and production efficiency of mines. With the continuous increase in the depth and scale of mine exploitation, mine ventilation faces increasingly complex challenges. Traditional ventilation systems are usually based on fixed control strategies and cannot meet the requirements of environmental changes and emergencies. For example, factors such as gas concentration, temperature and humidity, and working conditions in the mine change over time, and it is difficult for traditional control systems to adjust ventilation parameters in real time under these changes, resulting in energy waste and potential safety hazards.

[0003] Emergencies such as gas leakage, fire, or gas explosion in mines often require emergency ventilation adjustment. However, the traditional ventilation system has a weak emergency response ability to emergencies and usually relies on manual intervention, with slow processing speed and prone to errors. Although some mine ventilation systems have tried to introduce automated control technologies, due to insufficient data acquisition and analysis capabilities, these systems still cannot provide accurate judgments and efficient responses in complex and dynamically changing environments.

[0004] At the same time, the existing intelligent control methods for mine ventilation automation have the following technical problems: poor self - adaptability and low efficiency of ventilation control in complex environments. Summary of the Invention

[0005] To solve the technical problems described in the background art, the present invention provides an intelligent control system and method for mine ventilation automation.

[0006] The intelligent control system and method for mine ventilation automation of the present invention specifically include the following technical solutions:

[0007] An intelligent control method for mine ventilation automation includes the following steps:

[0008] S1. Pre - process the original data to obtain pre - processed data; perform intelligent analysis on the pre - processed data using a multi - adaptive dynamic game optimization algorithm to obtain an analysis result, and generate a final control strategy based on the analysis result;

[0009] S2. Introduce predictive feedback and real - time feedback to dynamically adjust the final control strategy to obtain an adjusted control strategy; based on the adjusted control strategy and the pre - processed data, obtain adjusted control instructions and emergency control instructions to adjust the control equipment in the mine.

[0010] Preferably, the S1 specifically includes:

[0011] The multi - adaptive dynamic game optimization algorithm analyzes the dynamic game of the control objectives, and combines the adaptive feedback mechanism of reinforcement learning to generate a ventilation control strategy in real time.

[0012] Preferably, the S1 specifically includes:

[0013] In the implementation process of the multi - adaptive dynamic game optimization algorithm, a game model is constructed, and the utility function of the game model is defined to obtain the utility value of the control objective. The specific implementation formula is:

[0014]

[0015] Where, is the control objective 's utility value; are the control strategies corresponding to each different control objective; is the total number of control objectives; is the weight value of the control objective ; is the control function of the control objective ; is the coefficient of the influence of the control objective on other control objectives ; is the influence coefficient of the control strategy of the control objective on the utility of other control objectives ; is the control function of the control objective ;

[0016] Preferably, the S1 specifically includes:

[0017] In the implementation process of the multi - adaptive dynamic game optimization algorithm, an adaptive game equilibrium strategy update mechanism is introduced, so that each control objective dynamically updates the control strategy of the control objective according to the feedback of the environment.

[0018] Preferably, the S1 specifically includes:

[0019] In the implementation process of the multi - adaptive dynamic game optimization algorithm, a weight adjustment mechanism is introduced to optimize the cooperation between control objectives in a dynamic environment, and the weights of each control objective are adjusted according to the interaction between control objectives and the feedback of real - time environmental data. The specific formula is:

[0020]

[0021] Where, is the control objective The weight in the th iteration; is the control target The weight in the th iteration; is the control target learning rate; is the control target utility value The partial derivative of the control target weight with respect to.

[0022] Preferably, the S2 specifically includes:

[0023] By introducing a mixed feedback formula, the control target is adjusted using predictive feedback and real-time feedback to obtain the adjusted environmental variables of the control target. The specific implementation formula is:

[0024]

[0025] where, is the adjusted environmental variable of the control target ; is the environmental variable corresponding to the control target , that is, the preprocessed data; is the real-time feedback weight coefficient; is the real-time feedback sensitivity coefficient; is the desired environmental variable; is the predictive feedback weight coefficient; is the control target predictive environmental variable.

[0026] Preferably, the S2 specifically includes:

[0027] The adjusted environmental variables are processed using the intelligent analysis processing process of the multi-adaptive dynamic game optimization algorithm in step S1 to obtain the adjusted control strategy, and the adjusted control instructions are generated according to the adjusted control strategy.

[0028] Preferably, the S2 specifically includes:

[0029] According to the preprocessed data, the safety standards of the mine, and the set threshold, the current environmental status is judged in real time to determine whether there is a dangerous situation. When a dangerous situation is detected, the emergency response mechanism is immediately activated, the severity of the situation in the mine is evaluated, and according to the evaluation results, emergency control instructions are generated to adjust the control equipment in the mine.

[0030] An intelligent control system for mine ventilation automation, comprising the following parts:

[0031] Data acquisition component, data preprocessing component, data intelligent analysis component, intelligent adjustment component, emergency response component;

[0032] The data acquisition component collects the environmental data inside the mine in real time as the raw data and sends the raw data to the data preprocessing component;

[0033] The data preprocessing component preprocesses the raw data to obtain the preprocessed data and sends the preprocessed data to the data intelligent analysis component and the emergency response component;

[0034] The data intelligent analysis component intelligently analyzes the preprocessed data using the multi - adaptive dynamic game optimization algorithm to obtain the analysis result, generates the final control strategy based on the analysis result, and at the same time introduces predictive feedback and real - time feedback to dynamically adjust the control strategy to obtain the adjusted control strategy; generates control instructions based on the adjusted control strategy and sends the control instructions to the intelligent adjustment component;

[0035] The intelligent adjustment component adjusts the control equipment in the mine according to the control instructions and the emergency control instructions to perform intelligent control of the mine ventilation automation;

[0036] The emergency response component, based on the preprocessed data, judges in real time whether there is a dangerous situation in the mine. When a dangerous situation is detected, it immediately activates the emergency response mechanism, generates emergency control instructions, and sends the emergency control instructions to the intelligent adjustment component.

[0037] The beneficial effects of the technical solution of the present invention are:

[0038] 1. By using the multi - objective dynamic game optimization algorithm to balance and optimize environmental variables such as gas concentration, temperature and humidity, oxygen concentration, etc., the mine ventilation intelligent control system always maintains the best control strategy under different environmental states, thereby improving the ventilation efficiency of the mine and reducing energy consumption; introducing the adaptive feedback mechanism of reinforcement learning enables the control strategy to be dynamically adjusted according to real - time environmental changes. Especially when the mine environment changes suddenly, it can adjust the control strategy in real time according to data feedback such as gas concentration, temperature and humidity, etc., improving the response ability of the ventilation intelligent control system to emergencies.

[0039] 2. By combining real - time feedback and predictive feedback, it can effectively predict and respond to dangerous situations such as gas leakage or insufficient oxygen concentration that may occur in the mine, and immediately activate the emergency response mechanism when the predetermined threshold is reached to ensure the safety of mine operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a structural diagram of an intelligent control system for mine ventilation automation described in the present invention;

[0041] Figure 2 This is a flow chart of an intelligent control method for mine ventilation automation according to the present invention. Specific embodiments

[0042] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] The following specifically describes the specific solutions of an intelligent control system and method for mine ventilation automation provided by the present invention in conjunction with the accompanying drawings.

[0045] Referring to the appendix Figure 1 , which shows a structural diagram of an intelligent control system for mine ventilation automation provided by an embodiment of the present invention. The system includes the following parts:

[0046] A data acquisition component, a data preprocessing component, a data intelligent analysis component, an intelligent adjustment component, and an emergency response component;

[0047] The data acquisition component collects environmental data inside the mine in real time through sensors such as gas concentration, temperature and humidity, oxygen content, etc., and uses it as raw data, including environmental data such as harmful gas concentration, oxygen level, temperature, humidity, and wind speed, and sends the raw data to the data preprocessing component;

[0048] The data preprocessing component performs preprocessing on the raw data, such as cleaning, denoising, outlier processing, and standardization, to obtain preprocessed data, and sends the preprocessed data to the data intelligent analysis component and the emergency response component;

[0049] The data intelligent analysis component performs intelligent analysis on the preprocessed data using a multi - adaptive dynamic game optimization algorithm to obtain an analysis result, generates a final control strategy based on the analysis result, and dynamically adjusts the control strategy by introducing prediction feedback and real - time feedback to obtain an adjusted control strategy; generates an adjusted control instruction based on the adjusted control strategy, and sends the adjusted control instruction to the intelligent adjustment component;

[0050] An intelligent adjustment component, a control device that adjusts the air volume and air velocity of a fan in a mine and the opening and closing states of valves according to adjusted control instructions and emergency control instructions to ensure that the air circulation and gas concentration in the mine are controlled within a safe range and realize the intelligent control of mine ventilation automation;

[0051] An emergency response component, which, based on the preprocessed data, determines in real time whether there are dangerous situations in the mine, such as excessive methane concentration and insufficient oxygen concentration. When a dangerous situation is detected, it immediately activates the emergency response mechanism, generates an emergency control instruction, and sends the emergency control instruction to the intelligent adjustment component to cope with sudden safety risks.

[0052] Refer to Appendix Figure 2 , which shows a flowchart of an intelligent control method for mine ventilation automation provided by an embodiment of the present invention. The method includes the following steps:

[0053] S1. Preprocess the original data to obtain preprocessed data; perform intelligent analysis on the preprocessed data using a multi - adaptive dynamic game optimization algorithm to obtain an analysis result, and generate a final control strategy based on the analysis result;

[0054] Collect environmental data inside the mine through sensors such as gas concentration sensors, temperature and humidity sensors, oxygen content sensors, and air velocity sensors, including environmental data such as harmful gas concentration, oxygen level, temperature, humidity, and air velocity, and use the environmental data as the original data; perform preprocessing on the original data, such as cleaning, denoising, outlier processing, and standardization, to obtain preprocessed data. The technical means used in the preprocessing process are well - known to those skilled in the art and will not be elaborated here.

[0055] Furthermore, perform intelligent analysis on the preprocessed data using a multi - adaptive dynamic game optimization algorithm to obtain an analysis result, and generate a final control strategy based on the analysis result; the multi - adaptive dynamic game optimization algorithm generates a ventilation control strategy in real time by performing dynamic game analysis on the control objectives of the mine ventilation intelligent control system and combining the adaptive feedback mechanism of reinforcement learning. The specific implementation process is as follows:

[0056] In the mine ventilation intelligent control system, multiple environmental variables need to be controlled, such as gas concentration, temperature and humidity, oxygen concentration, etc. These environmental variables are highly interconnected, and changes in each control objective in the environmental variables may have varying degrees of impact on other control objectives. Denote the th control objective in the environmental variables as , such as gas concentration, temperature and humidity, etc., and all can be characterized by a set of preprocessed data , which reflects the current operating state in the mine, where Indicates the total number of data after each type of preprocessing. Further, through the trade - off and optimization of the above - mentioned control objectives, a set of optimal ventilation control strategies are finally generated. The above - mentioned control objectives refer to the preprocessed data, representing the specific environmental parameters or control variables that the mine ventilation intelligent control system needs to optimize;

[0057] Furthermore, a game model is constructed; the game model regards the control objectives as participants in a game, and their control strategies are , that is, the control strategies of each control objective. For example, the control strategy for gas concentration may be the on - off state of the fan, and the control strategy for temperature may be the adjustment of wind speed. The utility function of this game model is defined as follows:

[0058]

[0059] where, is the utility value of the control objective , representing the optimization effect of the control objective under the current environmental state. The utility value determines whether the control objective can obtain the optimal ventilation control strategy in the multi - objective optimization game; is the control strategy corresponding to each different control objective, representing the optimization control measures taken for each control objective under the current environmental state. For example, wind speed adjustment, fan speed setting, air flow distribution, etc.; is the total number of control objectives; is the weight value of the control objective , representing the importance of this control objective in the entire mine ventilation intelligent control system, determined according to the expert experience method; is the control function of the control objective , representing the functional relationship between the control objective and its related environmental variables , fitted through existing physical modeling or data - driven models, such as derived from fluid mechanics, thermodynamics, or gas propagation models; is the coefficient of the influence of the control objective on other control objectives , representing the degree of interference or influence of the control objective on other control objectives during the ventilation optimization process, determined by the physical coupling relationship between control objectives and obtained through the experimental method; is the influence coefficient of the control strategy of the control objective on the utility of other control objectives , representing the influence of the control objective Control strategy For other control objectives The degree of influence on utility comes from the coupling relationship between control objectives. For example, air flow distribution may affect temperature and humidity, and temperature and humidity may affect gas concentration, which is determined by the experimental method; Is the control objective Control function;

[0060] Furthermore, in order to reasonably optimize multiple control objectives and find the equilibrium point of the game, the equilibrium point of the game refers to the situation where, given the control strategies of other control objectives, each control objective cannot improve its utility value by unilaterally changing the control strategy. To solve the equilibrium point of this game, an adaptive game equilibrium strategy update mechanism is introduced. Based on the idea of reinforcement learning, each control objective Dynamically updates its control strategy according to the feedback of the environment. The game equilibrium strategy update formula for each control objective is:

[0061]

[0062] Where Is the control strategy selected by the control objective In the th iteration, such as adjusting the speed of the mine fan, the distribution of air flow, the opening and closing of valves, etc.; Is the control strategy selected by the control objective In the th iteration; Is the learning rate of the control objective Used to adjust the amplitude of the control strategy update, indicating the adjustment step size of the control objective In each round of iteration update, determined by the expert experience method; Is the control objective Under the given current control strategy The gradient of the utility function, which measures the rate of change of the utility value with respect to the control strategy, and further guides the control objective to adjust the control strategy to maximize its own utility.

[0063] Specifically, the calculation formula for the gradient of the utility function is:

[0064]

[0065] Where Is the partial derivative of the utility value of the control objective with respect to the control strategy; Is the control objective The partial derivative of the control function with respect to the control strategy; Is the control objective The partial derivative of the control function with respect to the control strategy.

[0066] The game equilibrium strategy update process not only requires the optimization of the control objective with respect to its own control strategy but also needs to find an equilibrium point among multiple control objectives. To optimize the synergy among control objectives in a dynamic environment, a weight adjustment mechanism is introduced to adjust the weights of each control objective according to the interaction between control objectives and the feedback of real-time environmental data.

[0067] The formula for the weight adjustment mechanism is as follows:

[0068]

[0069] where, is the weight of control objective in the th iteration, representing the importance in the overall control strategy; is the weight of control objective in the th iteration. In particular, the initial weight is determined by the expert experience method; is the learning rate of control objective , which controls the amplitude of weight adjustment and is determined by the expert experience method; is the utility value of control objective is the partial derivative of the utility value of control objective with respect to the weight of control objective , used to measure the sensitivity of the utility value of control objective

[0070] The above process ensures that the weights of control objectives can be adaptively adjusted when the environment changes. For example, when the temperature in the mine is relatively high, it may be necessary to increase the weight of the temperature control objective and decrease the weight of the gas concentration control objective to ensure the priority of the ventilation effect.

[0071] Furthermore, after completing the game equilibrium strategy update and weight adjustment, the analysis result is obtained; and based on the analysis result, the final control strategy is generated. The specific content includes, based on the control strategy and weight of each control objective, using the existing multi-objective optimization technology and formulating constraints according to the expert experience method to ensure the feasibility and practical application value of the ventilation control strategy, and then using the existing Lagrange multiplier method to handle the relationship between the objective function and constraints, solving the optimal strategy, and obtaining the final control strategy.

[0072] S2. Introduce predictive feedback and real-time feedback to dynamically adjust the final control strategy to obtain the adjusted control strategy; based on the adjusted control strategy and the preprocessed data, obtain the adjusted control instruction and emergency control instruction to adjust the control equipment in the mine;​

[0073] By introducing a hybrid feedback formula, the control target is adjusted using predictive feedback and real-time feedback (such as changes in gas concentration, temperature, humidity, etc.) to obtain a more accurate control target. The specific formula is as follows:

[0074]

[0075] Where, is the control target is the adjusted environmental variable; is the control target corresponding environmental variable, that is, the preprocessed data; is the real-time feedback weight coefficient, which represents the sensitivity to the adjustment of the real-time feedback part and determines how the intelligent mine ventilation control system adjusts according to the error between the current environmental variable and the desired environmental variable, and is determined by the expert experience method; is the real-time feedback sensitivity coefficient, which is used to control the speed and amplitude of the real-time feedback adjustment and is determined by the experimental method; is the desired environmental variable, which is determined by the expert experience method; is the predictive feedback weight coefficient, which represents the sensitivity to the adjustment of the predictive feedback part and is determined by the expert experience method; is the control target predicted environmental variable, which is the predicted value of the future environmental variable based on the current environmental data and the historical environmental data taken from the existing database, and a time series prediction model can be used.

[0076] The adjusted environmental variable is used as the representation of the control target to reflect the current operating state of the mine, and the adjusted environmental variable is processed according to the processing process of intelligent analysis using the multi-adaptive dynamic game optimization algorithm in step S1 to obtain an adjusted control strategy, and an adjusted control instruction is generated according to the adjusted control strategy according to the existing control technology such as PID, such as a fan adjustment instruction (adjustment of wind speed, air volume, etc.), a valve adjustment instruction, and the adjusted control instruction is sent to the control equipment in the mine to adjust the wind speed, air volume of the fan in the mine and the opening and closing state of the valve. For example, when the methane concentration in a certain area of the mine is too high, it will instruct the fan to run faster and close some valves to improve the ventilation effect and realize the intelligent control of mine ventilation automation.

[0077] Based on the preprocessed data, the safety standards of the mine, and the thresholds set according to the expert experience method, it is determined in real time whether there are dangerous situations such as excessive methane concentration or insufficient oxygen concentration in the current environmental state. If a dangerous situation is detected, such as excessive methane concentration or insufficient oxygen, the emergency response mechanism is immediately activated. The emergency response mechanism will first evaluate the severity and possible development trend of the current situation in the mine through the existing prediction model. Then, according to the evaluation results, emergency control instructions are generated, including the adjustment of the fan's wind speed and air volume, and the change of the valve switch state. Specifically: the adjustment instructions for the fan will be determined based on the current environmental state (such as methane concentration, oxygen concentration, etc.). For example, when the methane concentration is too high, it will instruct the fan to operate at the maximum wind speed and increase the air volume to accelerate the gas emission and reduce the methane concentration; at the same time, some valves will be closed to prevent the further diffusion of dangerous gases in the mine and ensure the maximization of the ventilation effect.

[0078] In addition, the switch state of the valve will also be adjusted in the emergency control instructions. In some cases, it will be instructed to close certain valves to control the air flow direction in the mine, reduce the air leakage in the area with a higher methane concentration, and avoid affecting other areas. By intelligently adjusting control devices such as fans and valves, the expansion of danger can be effectively reduced, the concentration of harmful gases in the mine can be timely reduced, and the oxygen concentration can be restored to the normal level to ensure the safety of the mine.

[0079] In summary, an intelligent control system and method for mine ventilation automation are completed.

[0080] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An intelligent control method for mine ventilation automation, characterized in that: The following steps are involved: S1. Preprocessing the original data to obtain preprocessed data; The pre-processed data is intelligently analyzed using multiple adaptive dynamic game optimization algorithms to obtain analysis results, and the final control strategy is generated based on the analysis results; S2. Introduce prediction feedback and real-time feedback to dynamically adjust the final control strategy to obtain an adjusted control strategy; based on the adjusted control strategy and preprocessed data, obtain adjusted control instructions and emergency control instructions to adjust the control equipment in the mine; The multiple adaptive dynamic game optimization algorithm generates ventilation control strategies in real time by performing dynamic game analysis on the control target and combining the adaptive feedback mechanism of reinforcement learning; In the implementation process of the multiple adaptive dynamic game optimization algorithm, the game model is constructed, and the utility function of the game model is defined to obtain the utility value of the control target. The specific implementation formula is: ； in, It is the control target The utility value of It is the control strategy corresponding to different control objectives; is the total number of control targets; It is the control target The weight value of It is the control target The control function of It is the control target For other control objectives The coefficient of influence; It is the control target The control strategy for other control objectives The impact coefficient of utility; It is the control target The control function of Introducing an adaptive game equilibrium strategy update mechanism, so that each control target dynamically updates the control strategy of the control target according to the feedback from the environment; The weight adjustment mechanism is introduced to optimize the synergy between various control objectives in a dynamic environment. The weight of each control objective is adjusted according to the interaction between the control objectives and the feedback of real-time environmental data. The specific formula is: ； in, It is the control target In the The weight in the iteration; It is the control target In the The weight in the iteration; It is the control target The learning rate; It is the control target The utility value Weight of control target The partial derivative of .

2. The intelligent control method for mine ventilation automation according to claim 1, characterized in that: The S2 specifically includes: By introducing the mixed feedback formula, the control target is adjusted using the predicted feedback and real-time feedback to obtain the environmental variables after the control target is adjusted. The specific implementation formula is: ； in, It is the control target Adjusted environmental variables; It is the control target The corresponding environment variables, i.e. the preprocessed data; is the real-time feedback weight coefficient; is the real-time feedback sensitivity coefficient; are the desired environment variables; is the prediction feedback weight coefficient; It is the control target Predicted environmental variables.

3. The intelligent control method for mine ventilation automation according to claim 2, characterized in that: The S2 specifically includes: The adjusted environmental variables are processed by the intelligent analysis process of the multiple adaptive dynamic game optimization algorithm in step S1 to obtain the adjusted control strategy, and the adjusted control instructions are generated according to the adjusted control strategy.

4. The intelligent control method for mine ventilation automation according to claim 1, characterized in that: The S2 specifically includes: Based on the pre-processed data, the mine's safety standards and the set thresholds, it is determined in real time whether there is a dangerous condition in the current environmental state. When a dangerous condition is detected, the emergency response mechanism is immediately activated to assess the severity of the situation in the mine. Based on the assessment results, emergency control instructions are generated to adjust the control equipment in the mine.

5. An intelligent control system for mine ventilation automation, using the intelligent control method for mine ventilation automation according to claim 1, characterized in that: Includes the following parts: Data collection component, data preprocessing component, data intelligent analysis component, intelligent adjustment component, emergency response component; The data acquisition component collects the environmental data inside the mine in real time and sends the raw data to the data preprocessing component as raw data; The data preprocessing component preprocesses the raw data to obtain preprocessed data, and sends the preprocessed data to the data intelligent analysis component and the emergency response component; The data intelligent analysis component uses multiple adaptive dynamic game optimization algorithms to intelligently analyze the pre-processed data, obtain the analysis results, and generate the final control strategy based on the analysis results. At the same time, it introduces predictive feedback and real-time feedback to dynamically adjust the control strategy to obtain the adjusted control strategy; Generate control instructions based on the adjusted control strategy, and send the control instructions to the intelligent adjustment component; Intelligent adjustment components adjust the control equipment in the mine according to control instructions and emergency control instructions, and perform intelligent control of mine ventilation automation; The emergency response component determines in real time whether there is a dangerous condition in the mine based on the preprocessed data. When a dangerous condition is detected, the emergency response mechanism is immediately activated, emergency control instructions are generated, and emergency control instructions are sent to the intelligent adjustment component.

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