Coal mine underground environment intelligent sensing and dynamic ventilation linkage control system and method

Abstract

The invention discloses a coal mine underground environment intelligent sensing and dynamic ventilation linkage control system and method, and is applied to the technical field of coal mine ventilation. The system comprises an environment intelligent sensing module used for collecting environment data of an underground coal mine; the data transmission module is used for realizing data transmission among different modules; the underground data processing module is used for processing and analyzing the environmental data, and performing real-time ventilation early warning and real-time ventilation equipment regulation and control based on the environmental data; the ground linkage control module is used for predicting environment data of a future time node and setting a preset scheduling scheme of ventilation equipment in combination with a ventilation network model simulation result and an underground operation plan; and the dynamic ventilation regulation module is used for regulating the ventilation condition of the underground coal mine based on the preset scheduling scheme and the real-time regulation and control scheme. According to the method, prediction is carried out by collecting the environment data of the underground coal mine, the scheduling scheme is generated in advance according to the operation plan, and the hysteresis problem of a real-time ventilation regulation and control scheme is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine ventilation, and more particularly to a control system and method for intelligent perception and dynamic ventilation linkage of underground coal mine environment. Background Art

[0002] During coal mining, as the depth of the mine increases, the ventilation pressure also increases. Coal mining produces large amounts of harmful gases, which increases the risk of accidents, severely harms the health of workers, and leads to significant economic losses. Therefore, good ventilation is crucial for coal mining operations. Existing coal mine ventilation solutions primarily use sensor warnings combined with fan ventilation. Sensors are deployed throughout the underground tunnels. When the sensors detect that the concentration of hazardous gases exceeds a safety threshold or that environmental parameters such as temperature are abnormal, an alarm is issued to activate ventilation to reduce the probability of accidents. This ventilation control solution relies on real-time data acquisition from sensors and the generation of real-time control solutions. However, in actual coal mines, due to unstable data transmission, timely transmission of monitoring data is impossible. Furthermore, real-time control requires processing large amounts of data for calculations in a short period of time. As a result, this control solution has a certain degree of lag, low flexibility, and inability to proactively respond to various abnormal situations. Therefore, how to provide a control system and method for intelligent perception of the underground coal mine environment and dynamic ventilation linkage is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0003] In view of this, the present invention provides a control system and method for intelligent perception of the underground environment of a coal mine and dynamic ventilation linkage, which generates a preset scheduling plan based on intelligent perception of the environment and performs real-time regulation during the subsequent ventilation process, so that the ventilation equipment can promptly affect environmental changes.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] An intelligent perception and dynamic ventilation linkage control system for underground coal mine environment, comprising:

[0006] The environmental intelligent perception module is connected to the underground data processing module through the data transmission module to collect environmental data in the coal mine;

[0007] Data transmission module, used to realize data transmission between different modules;

[0008] The downhole data processing module is connected to the environmental intelligent perception module, the uphole linkage control module, and the ventilation equipment control module through the data transmission module. It is used to process and analyze environmental data and provide real-time ventilation warning and ventilation equipment control based on the environmental data.

[0009] The surface linkage control module is connected to the downhole data processing module through the data transmission module. It is used to predict environmental data at future time nodes and set a preset scheduling plan for ventilation equipment based on the simulation results of the ventilation network model and the downhole operation plan;

[0010] The dynamic ventilation adjustment module is connected to the underground data processing module through the data transmission module, and adjusts the ventilation conditions underground in the coal mine based on the preset scheduling plan of the ventilation equipment and the real-time ventilation equipment control plan.

[0011] Optionally, the environmental intelligent perception module includes a gas sensor, a temperature sensor, a humidity sensor, a wind speed sensor, an air volume sensor, a staff positioning device and a camera, wherein the gas sensor includes a gas sensor, a carbon monoxide sensor and a methane sensor.

[0012] Optionally, the downhole data processing module includes a data preprocessing unit, a hazardous gas real-time warning unit and a ventilation equipment real-time adjustment unit. The data preprocessing unit receives the environmental data collected by the environmental intelligent perception module and performs preprocessing operations. The hazardous gas real-time warning unit issues an early warning based on the real-time concentration of hazardous gases in the environmental data. The ventilation equipment real-time adjustment unit adjusts the operating status of the ventilation equipment based on the difference between the real-time concentration of hazardous gases and the warning value.

[0013] Optionally, the dangerous gas real-time warning unit issues a warning based on the real-time concentration of dangerous gases in the environmental data. Specifically, the dangerous gas warning level is divided into three levels: primary risk, intermediate risk and advanced risk. The primary risk concentration threshold, intermediate risk concentration threshold and advanced risk concentration threshold are set for manned working areas and unmanned working areas respectively. The real-time concentration of the dangerous gas is compared with the risk concentration threshold to obtain the current dangerous gas risk level. When the real-time concentration of the dangerous gas is lower than the primary risk concentration threshold, it is judged to be risk-free. According to the current dangerous gas risk level, a warning information is generated and sent to the well linkage control module and the ventilation equipment real-time adjustment unit. The ventilation equipment real-time adjustment unit has preset adjustment strategies corresponding to different risk levels.

[0014] Optionally, the operation status of the ventilation equipment of the real-time adjustment unit ventilation equipment is specifically:

[0015] In a risk-free state, maintain the preset scheduling plan unchanged;

[0016] In the primary and intermediate risk states, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the corresponding risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment to keep the real-time concentration of hazardous gases away from the risk concentration threshold.

[0017] Under high-risk conditions, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the high-risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment, changing the damper opening of the ventilation equipment, and changing the start and stop status of the ventilation equipment in the preset scheduling plan so that the real-time concentration of the hazardous gases is away from the high-risk concentration threshold.

[0018] Optionally, the surface linkage control module includes an environmental data prediction unit, a ventilation simulation unit and a ventilation equipment scheduling unit. The environmental data prediction unit predicts the environmental data of future time nodes based on the historical environmental data underground in the coal mine. The ventilation simulation unit simulates different ventilation equipment scheduling strategies through the ventilation system model. The ventilation equipment scheduling unit calculates the ventilation parameter demand data based on the predicted environmental data, and calculates the optimal ventilation scheduling strategy in combination with the underground operation plan and ventilation simulation.

[0019] Optionally, the environmental data prediction unit predicts the environmental data of future time nodes specifically as follows: the environmental data prediction unit divides the environmental data into univariate data and multivariate data according to the correlation situation. For univariate data prediction, the environmental data of the next time node is predicted based on the XGBoost model, and the input data is the time series data of the corresponding univariate data in the historical environmental data; for multivariate data prediction, the environmental data of the next time node is predicted based on the LSTM model, and the input data is the time series data of all variables associated with the corresponding multivariate data.

[0020] Optionally, the ventilation equipment scheduling unit calculates the optimal ventilation scheduling strategy as follows:

[0021] Divide the coal mine into multiple zones and determine the operating and non-operating areas based on the underground operation plan;

[0022] Calculate the required air volume for each zone based on the predicted dangerous gas concentration data in the environmental data at future time nodes within each zone;

[0023] Combined with the required air volume for each zone and the underground operation plan, the ventilation targets for the operating and non-operating areas are determined, and a ventilation equipment scheduling model is constructed:

[0024] minf(x)=ω1W+ω2S+ω3K

[0025]

[0026] Where W is the power function value, S is the safety function value, K is the adjustment quantity function value, ω1, ω2 and ω3 are the weights of W, S and K respectively, W0 is the total power of the ventilation equipment, W min The minimum value of the total power of the ventilation equipment under the current operating state, W maxis the maximum value of the total power of the ventilation equipment under the current operating state, Q l is the air volume in the lth area, H l is the wind pressure in the lth area, L is the number of areas, α is the concentration safety weight, β is the wind speed safety weight, L is the number of partitions, C l is the concentration of dangerous gas in the lth partition, C0 is the minimum warning concentration value of dangerous gas, v i is the wind speed of the ventilation equipment at the i-th node, n is the number of nodes, v min is the minimum wind speed requirement;

[0027] Constraints include hazardous gas concentration constraints in the operating area and non-operating area:

[0028] C l <C r ,l∈L A

[0029] C l <C0,l∈L B

[0030] Where C r is the safety reference value for hazardous gas operations, C r <C0,L A is the set of working areas, L B It is a set of non-operating areas; the ventilation equipment scheduling model is solved based on the genetic algorithm to obtain the preset scheduling plan.

[0031] Optionally, the ventilation simulation unit simulates different ventilation equipment scheduling strategies by constructing a three-dimensional model of the coal mine ventilation system, setting the start and stop status, frequency and damper opening of each ventilation equipment in the three-dimensional model in the ventilation equipment scheduling strategy, importing the environmental data of the coal mine for simulation, and calculating the changes in environmental data under the corresponding ventilation equipment scheduling strategy.

[0032] A method for intelligent perception of underground coal mine environment and dynamic ventilation linkage control, applied to the above-mentioned intelligent perception of underground coal mine environment and dynamic ventilation linkage control system, comprises the following steps:

[0033] Obtain real-time environmental data, historical environmental data, and future underground operation plans in coal mines;

[0034] Based on the historical environmental data of the coal mine, the environmental data of the future time nodes are predicted, and the ventilation parameter demand data of the future time nodes are calculated according to the environmental data of the future time nodes;

[0035] Determine ventilation targets for operating and non-operating areas based on ventilation parameter demand data and underground operation plans;

[0036] Design a ventilation scheduling model based on ventilation targets, and combine the ventilation network model simulation to solve the optimal ventilation scheduling strategy as the preset scheduling solution;

[0037] Based on the preset scheduling plan, real-time ventilation warning and real-time ventilation equipment adjustment are carried out based on the real-time environmental data of the coal mine.

[0038] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a control system and method for intelligent perception and dynamic ventilation linkage of underground coal mine environment, which has the following beneficial effects:

[0039] 1. This invention uses intelligent environmental sensing data to predict future environmental data and set preset scheduling plans for ventilation equipment. This allows for coordinated control of ventilation equipment in advance, adapting the ventilation plan to the current environment. When an abnormal situation occurs, dynamic adjustments only need to be made based on the preset scheduling plan based on real-time data, greatly reducing the computational burden of real-time control of ventilation equipment.

[0040] 2. This invention divides the coal mine into different zones. Combining the operation plan with the real-time positioning of the operators, the ventilation scheduling scheme is planned and adjusted in real time for the operation area and the unmanned area, thus ensuring the safety of the operators.

[0041] 3. The present invention reduces the total operating power of ventilation equipment and improves ventilation efficiency while ensuring the safety of coal mines by solving the optimal preset scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 This is a schematic diagram of the environmental intelligent perception and dynamic ventilation linkage control system of the present invention;

[0044] Figure 2 This is a flow chart for calculating the optimal ventilation scheduling strategy of the present invention;

[0045] Figure 3 This is a flow chart of the environmental intelligent perception and dynamic ventilation linkage control method of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] The embodiment of the present invention discloses a coal mine underground environment intelligent perception and dynamic ventilation linkage control system, such as Figure 1 As shown, including:

[0048] The environmental intelligent perception module is connected to the underground data processing module through the data transmission module to collect environmental data in the coal mine;

[0049] Data transmission module, used to realize data transmission between different modules;

[0050] The downhole data processing module is connected to the environmental intelligent perception module, the uphole linkage control module, and the ventilation equipment control module through the data transmission module. It is used to process and analyze environmental data and provide real-time ventilation warning and ventilation equipment control based on the environmental data.

[0051] The surface linkage control module is connected to the downhole data processing module through the data transmission module. It is used to predict environmental data at future time nodes and set a preset scheduling plan for ventilation equipment based on the simulation results of the ventilation network model and the downhole operation plan;

[0052] The dynamic ventilation adjustment module is connected to the underground data processing module through the data transmission module, and adjusts the ventilation conditions underground in the coal mine based on the preset scheduling plan of the ventilation equipment and the real-time ventilation equipment control plan.

[0053] Furthermore, the environmental intelligent perception module includes a gas sensor, a temperature sensor, a humidity sensor, a wind speed sensor, an air volume sensor, a staff positioning device and a camera, wherein the gas sensor includes a gas sensor, a carbon monoxide sensor and a methane sensor.

[0054] In an embodiment of the present invention, the temperature sensor adopts a thermistor temperature sensor to monitor the ambient temperature, and can also be set at key points of the ventilation equipment to monitor the equipment temperature. The humidity sensor adopts a capacitive humidity sensor to assist in judging the environmental status, and the humidity parameter is not used as an abnormal warning parameter; the air volume sensor adopts an ultrasonic air volume sensor; the staff positioning device is used to obtain the position of the staff. In this embodiment, the dangerous gas and temperature safety thresholds in the manned area of ​​the coal mine are lower than those in the unmanned area.

[0055] Furthermore, the downhole data processing module includes a data preprocessing unit, a real-time hazardous gas warning unit, and a real-time ventilation equipment adjustment unit. The data preprocessing unit receives environmental data collected by the intelligent environmental perception module and performs preprocessing operations. The real-time hazardous gas warning unit issues warnings based on the real-time concentration of hazardous gases in the environmental data. The real-time ventilation equipment adjustment unit adjusts the operating status of the ventilation equipment based on the difference between the real-time concentration of hazardous gases and the warning value. In an embodiment of the present invention, data preprocessing includes data filtering, compensation, and spatiotemporal alignment operations.

[0056] Furthermore, the dangerous gas real-time warning unit issues warnings based on the real-time concentration of dangerous gases in the environmental data. Specifically, the dangerous gas warning level is divided into three levels: primary risk, intermediate risk and advanced risk. The primary risk concentration threshold, intermediate risk concentration threshold and advanced risk concentration threshold are set for manned working areas and unmanned working areas respectively. The real-time concentration of the dangerous gas is compared with the risk concentration threshold to obtain the current dangerous gas risk level. When the real-time concentration of the dangerous gas is lower than the primary risk concentration threshold, it is judged to be risk-free. According to the current dangerous gas risk level, warning information is generated and sent to the well linkage control module and the ventilation equipment real-time adjustment unit. The ventilation equipment real-time adjustment unit has preset adjustment strategies corresponding to different risk levels.

[0057] In an embodiment of the present invention, the downhole data processing module also includes a temperature warning unit. The temperature warning unit presets temperature thresholds for manned working areas, unmanned working areas, and ventilation equipment temperature thresholds. When the temperature exceeds the corresponding threshold, a warning message is generated and sent to the uphole linkage control module and the ventilation equipment real-time adjustment unit.

[0058] Furthermore, the operation status of the ventilation equipment of the real-time adjustment unit ventilation equipment is specifically:

[0059] In a risk-free state, maintain the preset scheduling plan unchanged;

[0060] In the primary and intermediate risk states, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the corresponding risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment to keep the real-time concentration of hazardous gases away from the risk concentration threshold.

[0061] Under high-risk conditions, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the high-risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment, changing the damper opening of the ventilation equipment, and changing the start and stop status of the ventilation equipment in the preset scheduling plan so that the real-time concentration of the hazardous gases is away from the high-risk concentration threshold.

[0062] In an embodiment of the present invention, the frequency of the ventilation device is adjusted based on a PID control algorithm.

[0063] Furthermore, the surface linkage control module includes an environmental data prediction unit, a ventilation simulation unit and a ventilation equipment scheduling unit. The environmental data prediction unit predicts the environmental data of future time nodes based on the historical environmental data underground in the coal mine. The ventilation simulation unit simulates different ventilation equipment scheduling strategies through the ventilation system model. The ventilation equipment scheduling unit calculates the ventilation parameter demand data based on the predicted environmental data, and calculates the optimal ventilation scheduling strategy in combination with the underground operation plan and ventilation simulation.

[0064] Furthermore, the environmental data prediction unit predicts the environmental data of future time nodes specifically as follows: the environmental data prediction unit divides the environmental data into univariate data and multivariate data according to the correlation situation. For univariate data prediction, the environmental data of the next time node is predicted based on the XGBoost model, and the input data is the time series data of the corresponding univariate data in the historical environmental data; for multivariate data prediction, the environmental data of the next time node is predicted based on the LSTM model, and the input data is the time series data of all variables associated with the corresponding multivariate data.

[0065] In an embodiment of the present invention, the XGBoost model is used to predict temperature. The correlation between temperature and other parameters is not large. In the absence of abnormal conditions, its fluctuation is mainly related to its own historical change pattern. Therefore, the temperature values ​​of future time nodes are predicted by the XGBoost model, and its input is historical temperature time series data; the XGBoost model iteratively trains multiple weak learners through the gradient boosting algorithm, and optimizes the model's prediction ability through gradient descent.

[0066] In the embodiment of the present invention, the LSTM model is used to predict the concentration of hazardous gases at future time nodes. The concentration of hazardous gases is related to factors such as its historical time series data, the real-time working status of the ventilation equipment, and the real-time topology of the ventilation system. Therefore, prediction is made based on the LSTM model. The LSTM model includes three gate structures, and its formula is:

[0067] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0068] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0069] o t =σ(W o ·[ht-1 ,x t ]+b o )

[0070] Where, f t 、i t 、o t They are forget gate, input gate, output gate, W f 、W i 、W o are the weights of the forget gate, input gate, and output gate, respectively, b f 、b i 、b o are the biases of the forget gate, input gate, and output gate respectively, σ is the Sigmoid activation function, and h t-1 is the hidden state at time step t-1, x t is the input feature at time step t; the input gate generates new candidate values ​​by updating the unit state The state value c based on time step t-1 t-1 , candidate values Calculate the state value c at time step t t :

[0071]

[0072] The state value c based on time step t t and the calculated hidden state h t :

[0073] h t =o t *tanh(C t )

[0074] Where W C is the weight of the candidate value, b C is the bias of the candidate value.

[0075] Further, such as Figure 2 As shown, the ventilation equipment scheduling unit calculates the optimal ventilation scheduling strategy as follows:

[0076] Divide the coal mine into multiple zones and determine the operating and non-operating areas based on the underground operation plan;

[0077] Calculate the required air volume for each zone based on the predicted dangerous gas concentration data in the environmental data at future time nodes within each zone;

[0078] In an embodiment of the present invention, the air volume required for each partition, i.e., the air volume required to reduce the concentration of hazardous gases to a safe concentration, is the sum of the current air volume and the air volume deficit. The current air volume is the air volume generated by the ventilation equipment's fan in its current state, and the air volume deficit is calculated as the difference between the concentration of the hazardous gas at the next time point and the safe concentration. Depending on the ventilation system's scheduling scheme, the air volume that can be generated also varies. When subsequently solving the optimal scheduling scheme, it is necessary to ensure that the generated air volume exceeds the air volume required for each partition.

[0079] Combined with the required air volume for each zone and the underground operation plan, the ventilation targets for the operating and non-operating areas are determined, and a ventilation equipment scheduling model is constructed:

[0080] minf(x)=ω1W+ω2S+ω3K

[0081]

[0082] Where W is the power function value, S is the safety function value, K is the adjustment quantity function value, ω1, ω2 and ω3 are the weights of W, S and K respectively, W0 is the total power of the ventilation equipment, W min The minimum value of the total power of the ventilation equipment under the current operating state, W max is the maximum value of the total power of the ventilation equipment under the current operating state, Q l is the air volume in the lth area, H l is the wind pressure in the lth area, L is the number of areas, α is the concentration safety weight, β is the wind speed safety weight, L is the number of partitions, C l is the concentration of dangerous gas in the lth partition, C0 is the minimum warning concentration value of dangerous gas, v i is the wind speed of the ventilation equipment at the i-th node, n is the number of nodes, v min is the minimum wind speed requirement;

[0083] Constraints include hazardous gas concentration constraints in the operating area and non-operating area:

[0084] C l <C r ,l∈L A

[0085] C l <C0,l∈L B

[0086] Where C r is the safety reference value for hazardous gas operations, C r <C0,L A is the set of working areas, L BIt is a set of non-operating areas; the ventilation equipment scheduling model is solved based on the genetic algorithm to obtain the preset scheduling plan.

[0087] In the embodiment of the present invention, the constraint conditions further include:

[0088] v i ≥v min

[0089] f min ≤f i ≤f max

[0090] θ min ≤θ i ≤θ max

[0091] k min ≤k≤k max

[0092] Where, f i is the frequency of the ventilation equipment at the i-th node, θ i is the opening degree of the ventilation equipment at the i-th node, k is the number of equipment adjustments, f min ,θ min 、k min are the minimum values ​​of frequency, opening, and equipment adjustment quantity, respectively, f max ,θ max 、k max They are the maximum values ​​of frequency, opening, and equipment adjustment quantity respectively.

[0093] In an embodiment of the present invention, the well linkage control module also includes a human-computer interaction unit, which displays real-time environmental data, alarm information, a three-dimensional model of the ventilation control system, and a simulation process based on a graphical interface, and supports viewing historical data in an icon format. The staff can manually control the working status of the ventilation equipment based on the unit.

[0094] Furthermore, the ventilation simulation unit simulates different ventilation equipment scheduling strategies specifically by: constructing a three-dimensional model of the coal mine ventilation system, setting the start and stop status, frequency and damper opening of each ventilation equipment in the three-dimensional model in the ventilation equipment scheduling strategy, importing the environmental data of the coal mine for simulation, and calculating the changes in environmental data under the corresponding ventilation equipment scheduling strategy.

[0095] and Figure 1 Corresponding to the above system, the embodiment of the present invention also discloses a method for intelligent perception and dynamic ventilation linkage control of coal mine underground environment, which is applied to the above-mentioned intelligent perception and dynamic ventilation linkage control system of coal mine underground environment, such as Figure 3 As shown, the following steps are included:

[0096] Obtain real-time environmental data, historical environmental data, and future underground operation plans in coal mines;

[0097] Based on the historical environmental data of the coal mine, the environmental data of the future time nodes are predicted, and the ventilation parameter demand data of the future time nodes are calculated according to the environmental data of the future time nodes;

[0098] Determine ventilation targets for operating and non-operating areas based on ventilation parameter demand data and underground operation plans;

[0099] Design a ventilation scheduling model based on ventilation targets, and combine the ventilation network model simulation to solve the optimal ventilation scheduling strategy as the preset scheduling solution;

[0100] Based on the preset scheduling plan, real-time ventilation warning and real-time ventilation equipment adjustment are carried out based on the real-time environmental data of the coal mine.

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. The methods disclosed in the embodiments are described briefly because they correspond to the systems disclosed in the embodiments. For relevant parts, refer to the description of the systems.

[0102] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A coal mine underground environment intelligent perception and dynamic ventilation linkage control system, characterized in that: include: The environmental intelligent perception module is connected to the underground data processing module through the data transmission module to collect environmental data in the coal mine; Data transmission module, used to realize data transmission between different modules; The downhole data processing module is connected to the environmental intelligent perception module, the uphole linkage control module, and the ventilation equipment control module through the data transmission module. It is used to process and analyze environmental data and provide real-time ventilation warning and ventilation equipment control based on the environmental data. The surface linkage control module is connected to the downhole data processing module through the data transmission module. It is used to predict environmental data at future time nodes and set a preset scheduling plan for ventilation equipment based on the simulation results of the ventilation network model and the downhole operation plan; The dynamic ventilation adjustment module is connected to the underground data processing module through the data transmission module, and adjusts the ventilation conditions underground in the coal mine based on the preset scheduling plan of the ventilation equipment and the real-time ventilation equipment control plan.

2. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 1 is characterized in that: The environmental intelligent perception module includes a gas sensor, a temperature sensor, a humidity sensor, a wind speed sensor, an air volume sensor, a staff positioning device and a camera. Among them, the gas sensor includes a gas sensor, a carbon monoxide sensor and a methane sensor.

3. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 1 is characterized in that: The downhole data processing module includes a data preprocessing unit, a dangerous gas real-time warning unit and a ventilation equipment real-time adjustment unit. The data preprocessing unit receives the environmental data collected by the environmental intelligent perception module and performs preprocessing operations. The dangerous gas real-time warning unit issues an early warning based on the real-time concentration of dangerous gases in the environmental data. The ventilation equipment real-time adjustment unit adjusts the operating status of the ventilation equipment based on the difference between the real-time concentration of dangerous gases and the warning value.

4. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 3 is characterized in that: The real-time early warning unit for hazardous gases issues early warnings based on the real-time concentration of hazardous gases in environmental data. Specifically, the hazardous gas early warning levels are divided into three levels: primary risk, intermediate risk, and advanced risk. Primary risk concentration thresholds, intermediate risk concentration thresholds, and advanced risk concentration thresholds are set for manned operating areas and unmanned operating areas, respectively. The real-time concentration of hazardous gases is compared with the risk concentration threshold to obtain the current hazardous gas risk level. When the real-time concentration of hazardous gases is lower than the primary risk concentration threshold, it is determined to be risk-free. Early warning information is generated based on the current hazardous gas risk level and sent to the well linkage control module and the real-time adjustment unit for ventilation equipment. The real-time adjustment unit for ventilation equipment has preset adjustment strategies corresponding to different risk levels.

5. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 4 is characterized in that: The operating status of the ventilation equipment real-time adjustment unit ventilation equipment is as follows: In a risk-free state, maintain the preset scheduling plan unchanged; In the primary and intermediate risk states, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the corresponding risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment to keep the real-time concentration of hazardous gases away from the risk concentration threshold. Under high-risk conditions, the target air volume is determined based on the difference between the real-time concentration of hazardous gases and the high-risk concentration threshold. The air volume is adjusted by increasing the frequency of the ventilation equipment, changing the damper opening of the ventilation equipment, and changing the start and stop status of the ventilation equipment in the preset scheduling plan so that the real-time concentration of the hazardous gases is away from the high-risk concentration threshold.

6. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 1 is characterized in that: The surface linkage control module includes an environmental data prediction unit, a ventilation simulation unit and a ventilation equipment scheduling unit. The environmental data prediction unit predicts the environmental data of future time nodes based on the historical environmental data underground in the coal mine. The ventilation simulation unit simulates different ventilation equipment scheduling strategies through the ventilation system model. The ventilation equipment scheduling unit calculates the ventilation parameter demand data based on the predicted environmental data, and calculates the optimal ventilation scheduling strategy based on the underground operation plan and ventilation simulation.

7. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 6, characterized in that: The environmental data prediction unit predicts the environmental data of future time nodes specifically as follows: the environmental data prediction unit divides the environmental data into univariate data and multivariate data according to the correlation situation. For univariate data prediction, the environmental data of the next time node is predicted based on the XGBoost model, and the input data is the time series data of the corresponding univariate data in the historical environmental data; for multivariate data prediction, the environmental data of the next time node is predicted based on the LSTM model, and the input data is the time series data of all variables associated with the corresponding multivariate data.

8. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 6, characterized in that: The ventilation equipment scheduling unit calculates the optimal ventilation scheduling strategy as follows: Divide the coal mine into multiple zones and determine the operating and non-operating areas based on the underground operation plan; Calculate the required air volume for each zone based on the predicted dangerous gas concentration data in the environmental data at future time nodes within each zone; Combined with the required air volume for each zone and the underground operation plan, the ventilation targets for the operating and non-operating areas are determined, and a ventilation equipment scheduling model is constructed: minf(x)=ω1W+ω2S+ω3K Where W is the power function value, S is the safety function value, K is the adjustment quantity function value, ω1, ω2 and ω3 are the weights of W, S and K respectively, W0 is the total power of the ventilation equipment, W min The minimum value of the total power of the ventilation equipment under the current operating state, W max is the maximum value of the total power of the ventilation equipment under the current operating state, Q l is the air volume in the lth area, H l is the wind pressure in the lth area, L is the number of areas, α is the concentration safety weight, β is the wind speed safety weight, L is the number of partitions, C l is the concentration of dangerous gas in the lth partition, C0 is the minimum warning concentration value of dangerous gas, v i is the wind speed of the ventilation equipment at the i-th node, n is the number of nodes, v min is the minimum wind speed requirement; Constraints include hazardous gas concentration constraints in the operating area and non-operating area: C l <C r ,l∈L A C l <C0,l∈L B Where C r is the safety reference value for hazardous gas operations, C r <C0,L A is the set of working areas, L B It is a set of non-operating areas; the ventilation equipment scheduling model is solved based on the genetic algorithm to obtain the preset scheduling plan.

9. The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment according to claim 6, characterized in that: The ventilation simulation unit simulates different ventilation equipment scheduling strategies specifically by: constructing a three-dimensional model of the coal mine ventilation system, setting the start and stop status, frequency and damper opening of each ventilation equipment in the three-dimensional model in the ventilation equipment scheduling strategy, importing the environmental data of the coal mine for simulation, and calculating the changes in environmental data under the corresponding ventilation equipment scheduling strategy.

10. A method for intelligent perception and dynamic ventilation linkage control of underground coal mine environment, characterized in that: The intelligent perception and dynamic ventilation linkage control system for underground coal mine environment as claimed in any one of claims 1 to 9 comprises the following steps: Obtain real-time environmental data, historical environmental data, and future underground operation plans in coal mines; Based on the historical environmental data of the coal mine, the environmental data of the future time nodes are predicted, and the ventilation parameter demand data of the future time nodes are calculated according to the environmental data of the future time nodes; Determine ventilation targets for operating and non-operating areas based on ventilation parameter demand data and underground operation plans; Design a ventilation scheduling model based on ventilation targets, and combine the ventilation network model simulation to solve the optimal ventilation scheduling strategy as the preset scheduling solution; Based on the preset scheduling plan, real-time ventilation warning and real-time ventilation equipment adjustment are carried out based on the real-time environmental data underground in the coal mine.

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