An intelligent control system and control method for coordinated extraction of coal mine gas in different zones

Abstract

The present invention discloses an intelligent control system and control method for coordinated zoning extraction of coal mine gas, which relates to the technical field of coal mines. The system comprises: a zoning extraction sensor module, which is used to collect real-time extraction parameters of various underground areas and real-time operating data of extraction pump stations; a zoning extraction gas parameter classification and processing module, which is used to classify and process the real-time extraction parameters of various underground areas and the operating data of the extraction pump stations according to the characteristics of the zoning extraction sensor module and the characteristics of the data included in the real-time extraction parameters, and obtain classification processing results; a zoning extraction intelligent control module, which is used to adjust the real-time operating status of the extraction pump stations and the real-time operating status of each extraction zone according to the classification processing results; a zoning extraction early warning and emergency response module, which is used to start the early warning mechanism when the real-time extraction parameters, the real-time operating status of the extraction pump stations or the real-time operating status of each extraction zone meet the early warning conditions.

Description

Technical Field

[0001] The present application relates to the field of coal mine technology, and more specifically, to an intelligent control system and control method for coordinated zoning extraction of coal mine gas. Background Art

[0002] Coal mine gas extraction is a critical component of coal mine safety. Its primary purpose is to extract gas from coal seams to the surface through specific technical means, eliminating the risk of coal seam outbursts, reducing the amount of coal seam gas released into the mine, preventing gas explosions, and ensuring miner safety. It also allows for the utilization of gas as an energy source. Currently, coal mine gas extraction technologies primarily include extraction through boreholes within the coal seam, extraction through interbedded boreholes, extraction through high-level fissure boreholes, and extraction from goafs. Gas that emerges from roadways is diluted through ventilation.

[0003] Existing gas extraction systems automatically adjust valves based on the gas concentration of each borehole (or group of boreholes). Specifically, when sensors detect low gas concentrations, the system automatically closes the corresponding extraction valve to conserve resources; when concentrations are high, the system automatically opens the valve to allow extraction.

[0004] The terminal adjustment of this gas extraction system can invert the total amount of gas extracted from each borehole (or each group of connected boreholes) and evaluate the extraction effect in real time based on parameters such as the extracted gas concentration, but it cannot adaptively and dynamically adjust the extraction parameters of the entire extraction zone.

[0005] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0006] The present application provides an intelligent control system and control method for coordinated zoned extraction of coal mine gas to solve the above-mentioned technical problems.

[0007] In the first aspect, the present application provides an intelligent control system for coordinated zoned extraction of coal mine gas, including: a zoned extraction sensor module, which is used to collect real-time extraction parameters of each area underground and real-time operating data of the extraction pump station; a zoned extraction gas parameter classification and processing module, which is used to classify and process the real-time extraction parameters of each area underground and the operating data of the extraction pump station according to the characteristics of the zoned extraction sensor module and the characteristics of the real-time extraction parameters including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data and temperature data, to obtain a classification processing result; a zoned extraction intelligent control module, which is used to adjust the real-time operating status of the extraction pump station and the real-time operating status of each extraction zone according to the classification processing result; wherein the extraction pump station includes multiple extraction zones; a zoned extraction early warning and emergency response module, which is used to activate the early warning mechanism when the real-time extraction parameters, the real-time operating status of the extraction pump station or the real-time operating status of each extraction zone meet the early warning conditions.

[0008] On the second aspect, the present application provides an intelligent control method for coordinated extraction of coal mine gas in different zones, including: collecting real-time extraction parameters of each area underground and real-time operation data of the extraction pump station based on the zone extraction sensor module; classifying and processing the real-time extraction parameters of each area underground and the operation data of the extraction pump station according to the characteristics of the zone extraction sensor module and the characteristics of the real-time extraction parameters including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data and temperature data to obtain classification processing results; adjusting the real-time operation status of the extraction pump station and the real-time operation status of each extraction zone according to the classification processing results; wherein, the extraction pump station includes multiple extraction zones; when the real-time extraction parameters, the real-time operation status of the extraction pump station or the real-time operation status of each extraction zone meet the warning conditions, the warning mechanism is activated.

[0009] Based on the embodiments provided in the present application, based on the partition extraction sensor module, the partition extraction gas parameter classification and processing module, the partition extraction intelligent control module and the partition extraction early warning and emergency response module, the real-time extraction parameters of different areas are collected through the partition extraction sensor module, thereby realizing the coordinated management of gas extraction in various areas underground, and improving the pertinence and efficiency of extraction; the partition extraction gas parameter classification and processing module is used to classify and process the collected data, so that the control decision is based on data-driven, which improves the scientificity and accuracy of the control; the partition extraction intelligent control module can adjust the extraction pump in real time according to the classification processing results. The operating status of the station and the real-time operating status of each extraction zone make extraction operations more flexible and responsive; the zone extraction warning and emergency response module can quickly activate the warning mechanism when the warning conditions are met, improving the ability to respond to emergencies and enhancing the safety of the mine; the real-time operating status of the extraction pump station and the real-time operating status of each extraction zone are adaptively controlled according to the actual production needs of different regions, enhancing the adaptability and flexibility of the system; the real-time data and classification processing results provided by the system can provide decision makers with a more comprehensive and in-depth analysis of gas extraction conditions, supporting more scientific and reasonable decision-making. Through intelligent control and warning emergency response, the system can reduce fluctuations in the extraction process and improve the stability of the entire gas extraction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0011] Figure 1 This is a structural diagram of an optional intelligent control system for coordinated zoning extraction of coal mine gas according to an embodiment of the present application;

[0012] Figure 2 This is a flow chart of an optional intelligent control system for coordinated zoning extraction of coal mine gas according to an embodiment of the present application.

[0013] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0014] In order to enable people skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0015] Alternatively, as Figure 1 As shown, the present application provides an intelligent control system for coordinated extraction of coal mine gas by zones, including:

[0016] The zoned extraction sensor module 101 is used to collect real-time extraction parameters of each area underground and real-time operation data of the extraction pump station;

[0017] The zoned gas extraction parameter classification and processing module 102 is used to classify and process the real-time extraction parameters of each area in the well and the operating data of the extraction pump station based on the characteristics of the zoned extraction sensor module and the characteristics of the real-time extraction parameters, including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data, and temperature data, to obtain classification processing results;

[0018] The zoned drainage intelligent control module 103 is used to adjust the real-time operating status of the drainage pump station and the real-time operating status of each drainage zone according to the classification processing results; wherein the drainage pump station includes multiple drainage zones;

[0019] The zoned extraction warning and emergency response module 104 is used to activate the warning mechanism when the real-time extraction parameters, the real-time operating status of the extraction pump station or the real-time operating status of each extraction zone meet the warning conditions.

[0020] The pumping station includes multiple extraction zones with corresponding groups of sensors deployed. For example, extraction zone 1 is deployed with group 1 sensors, while extraction zone 2 is deployed with group 2 sensors.

[0021] The deployment locations of each group of sensors corresponding to each extraction zone can refer to the requirements of the Coal Mine Safety Regulations.

[0022] It should be understood that since the time and number of drilling holes for starting gas extraction in each extraction zone are usually different, it is necessary to handle each extraction zone according to its situation.

[0023] Based on the embodiments provided in the present application, based on the partition extraction sensor module, the partition extraction gas parameter classification and processing module, the partition extraction intelligent control module and the partition extraction early warning and emergency response module, the real-time extraction parameters of different areas are collected through the partition extraction sensor module, thereby realizing the coordinated management of gas extraction in various areas underground, and improving the pertinence and efficiency of extraction; the partition extraction gas parameter classification and processing module is used to classify and process the collected data, so that the control decision is more data-driven, and the scientificity and accuracy of the control are improved; the partition extraction intelligent control module can adjust the extraction in real time according to the classification processing results. The operating status of the pumping station and the real-time operating status of each extraction zone make extraction operations more flexible and responsive. The zone extraction warning and emergency response module can quickly activate the warning mechanism when the warning conditions are met, improving the ability to respond to emergencies and enhancing the safety of the mine. The real-time operating status of the extraction pump station and the real-time operating status of each extraction zone are adaptively adjusted according to the actual production needs of different regions, enhancing the adaptability and flexibility of the system. The real-time data and classification processing results provided by the system can provide decision makers with a more comprehensive and in-depth analysis of gas extraction conditions, supporting more scientific and reasonable decision-making. Through intelligent control and warning emergency response, the system can reduce fluctuations in the extraction process and improve the stability of the entire gas extraction system.

[0024] Furthermore, the zoned gas extraction parameter classification and processing module classifies and processes the real-time extraction parameters of each area underground and the operating data of the extraction pump station based on the characteristics of the zoned extraction sensor module and the characteristics of the real-time extraction parameters, including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data, and temperature data, to obtain the classification processing results, which are configured as follows:

[0025] Determining the strength of correlation between the deployment location of each sensor included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameters;

[0026] Creating an association matrix; wherein the rows of the association matrix represent the deployment locations of the sensors included in the partitioned sampling sensor module, and the columns of the association matrix represent the characteristics of the data included in the real-time sampling parameters; and the elements in the association matrix represent the strength of the association between the deployment locations of the sensors included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameters;

[0027] For each sensor deployment location included in the partitioned sampling sensor module, the characteristics of the data included in the real-time sampling parameters corresponding to the sensor deployment location in the association matrix are processed as transactions to construct an FP tree. Each node of the FP tree represents a characteristic, and the node weight represents the frequency of the characteristic appearing in the transaction.

[0028] By constructing the obtained FP tree, the feature combinations that co-occur in the deployment locations of multiple sensors are mined;

[0029] Based on a combination of characteristics that commonly appear in the deployment locations of multiple sensors, the same extraction mode is assigned to the extraction partitions having the common characteristics;

[0030] For each feature combination, a conditional pattern base is constructed; the conditional pattern base is based on a given feature combination and mines frequent combinations of other features;

[0031] The real-time extraction parameters of each area underground and the operating data of the extraction pump station are matched with the frequent combinations and characteristic combinations of other features mined, and the real-time extraction parameters of each area underground and the operating data of the extraction pump station are classified and processed according to the matching results.

[0032] The characteristics of the data included in the real-time extraction parameters may include changes in the extraction gas concentration, the law of extraction volume attenuation, abnormal carbon monoxide concentration, and changes in the extraction negative pressure.

[0033] The characteristics of the data included in the real-time extraction parameters may also include predictive changes, gas release patterns, gas outburst anomalies, coal seam gas desorption characteristics, and coal seam permeability changes.

[0034] Gas release pattern: refers to the release pattern and rate of gas in coal seams, which is related to predictive changes and can indicate potential gas accumulation or leakage risks.

[0035] Abnormal gas outflow: Monitor whether there is an abnormal increase in gas outflow, which may be a sign of a gas explosion.

[0036] Coal seam gas desorption characteristics: The adsorption and desorption behavior of gas in coal seams are closely related to gas concentration and environmental factors.

[0037] Changes in coal seam permeability: Changes in coal seam permeability affect gas migration and extraction efficiency, and are closely related to gas concentration and environmental factors.

[0038] By constructing the obtained FP tree, the feature combinations that co-occur in the deployment locations of multiple sensors are mined based on the following formula;

[0039]

[0040] Among them, F pattern represents the frequency score of the feature combination; M is the total number of sensor deployment locations; N is the total number of features of the data included in the real-time sampling parameters; freq mn is the frequency of occurrence of concentration characteristic n at the sensor deployment location m; conf mnIt is the conditional frequency of characteristic n in the deployment position m of the sensor, that is, the ratio of the frequency of characteristic n in the deployment position m of the sensor to the total frequency of all characteristics in the deployment position m of the sensor.

[0041] Furthermore, determining the correlation strength between the deployment position of each sensor included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameters is configured as follows:

[0042] Preprocess historical coal mine gas extraction monitoring data to handle outliers and missing values, including treating outliers as missing values ​​and applying sliding Lagrange interpolation to interpolate missing values. The historical coal mine gas extraction monitoring data includes extracted gas concentration and flow data.

[0043] For each sensor deployment location, an ARI MA model is constructed to analyze the time series characteristics of the extracted gas concentration and flow rate;

[0044] Applying a sequential learning algorithm, the model parameters of the ARI MA model are adjusted according to changes in gas concentration, attenuation of extraction volume, abnormal carbon monoxide concentration, and changes in extraction negative pressure.

[0045] Based on the adjusted ARI MA model, the strength of association between the deployment location of each sensor and the characteristics of the data included in the real-time sampling parameters is determined.

[0046] Furthermore, the ARI MA model can be expressed as:

[0047]

[0048] Among them, B is the backoff operator; is the autoregressive coefficient; θ j is the moving average coefficient; ∈ t is white noise; Y t is the extracted gas concentration and flow data at time t; p is the number of autoregressive terms, indicating how many time points in the past the current extracted gas concentration and flow data are related to the historical extracted gas concentration and flow data; i is the cumulative index; d is the number of differences required for the time series to become a stationary series; q is the number of moving average terms, indicating how many time points in the past the current extracted gas concentration and flow data are related to the prediction error; j is the cumulative index.

[0049] Furthermore, for each feature combination, a conditional pattern base is constructed, which is configured as follows:

[0050] Utilize the FP-Growth algorithm to extract feature combinations that meet the preset minimum support threshold from the FP tree;

[0051] For each feature combination that meets the preset minimum support threshold, a conditional FP tree is constructed; wherein the conditional FP tree is a subtree of the FP tree, including paths related to the feature combination that meets the preset minimum support threshold;

[0052] Based on the conditional FP tree, a candidate item set is generated; wherein the candidate item set includes all other features that can appear together with the frequent feature combination that meets the preset minimum support threshold;

[0053] For each item set in the candidate item set, calculate its conditional support; where the conditional support is the frequency of the item set appearing in the candidate item set under the condition of a given frequent feature combination;

[0054] According to the preset minimum support threshold, the item sets whose conditional support is higher than the preset minimum support threshold are screened out; wherein, the item sets whose conditional support is higher than the threshold constitute the frequent item sets in the conditional pattern base;

[0055] Add the filtered frequent itemsets to the conditional pattern base.

[0056] The intelligent control module for zoned drainage adjusts the real-time operating status of the drainage pumping station based on the classification processing results. It is configured as follows:

[0057] Receive the classification and processing results output from the sub-area gas extraction parameter classification and processing module;

[0058] Set control objectives and operational constraints for gas extraction according to coal mine safety standards and operating procedures;

[0059] Among them, there are control objectives for gas extraction, such as keeping gas concentration within a safe range; operational constraints, such as the maximum and minimum operating speeds of the pumping station;

[0060] Construct a model to describe the relationship between the operating status of the drainage pump station and the gas drainage parameters;

[0061] Based on the model and classification results used to describe the relationship between the operating status of the extraction pump station and the gas extraction parameters, the changing trend of gas concentration and flow rate in the future period is predicted;

[0062] Define optimization objectives, including the maximum deviation of gas concentration and flow rate and the minimum energy consumption of the pumping station;

[0063] Among them, for the target of the maximum deviation of gas concentration and flow rate, that is, the target of the pure amount of extracted gas, the pure amount of extracted gas cannot be too low. Too low pure amount of extracted gas indicates that the operation effect of the extraction system is not ideal;

[0064] Calculate the optimal operating parameters of the extraction pump station based on the predicted gas concentration and flow rate trends and the solution to the optimization objective;

[0065] An adjustment strategy is implemented based on the optimal operating parameters and operating constraints of the extraction pump station; among them, the adjustment strategy includes the negative pressure adjustment strategy for the extraction zone, the pumping stop decision strategy, and the pump station operation status adjustment strategy.

[0066] Negative pressure adjustment: If the prediction results show that the gas concentration may exceed the safe range, increase the negative pressure to improve the extraction efficiency; if the gas concentration is lower than the preset value, reduce the negative pressure to save energy.

[0067] Suspension extraction decision strategy: When the gas concentration continues to be lower than the effective extraction level and is not predicted to change significantly in the future, an automatic decision is made to stop extraction to save resources.

[0068] Pump station operation status adjustment strategy: Synchronize with the negative pressure adjustment and adjust the operation status of the extraction pump, including pump speed and valve position, to ensure that the extraction system operates in the optimal state.

[0069] Among them, based on the model used to describe the relationship between the operating status of the extraction pump station and the gas extraction parameters and the classification processing results, the changing trend of gas concentration and flow in the future period is predicted, including:

[0070] Using the classification results output by the zoned gas extraction parameter classification and processing module, key features are extracted and used as inputs for the following models.

[0071] Build a neural network model that combines multi-head attention and bidirectional GRU. The model architecture is as follows:

[0072] Bidirectional GRU layer: Uses bidirectional gated recurrent units to capture the forward and backward dependencies of time series data. This enables the model to consider both historical and future information, enhancing its ability to predict gas concentration and flow rate trends. Multi-head attention layer: Based on the bidirectional GRU, it introduces a multi-head attention mechanism to enhance the model's ability to identify key time points and features by processing information from multiple subspaces in parallel.

[0073] Use historical data to train the multi-head attention-bidirectional GRU model, optimize the network weights through the backpropagation algorithm, and improve the model's prediction accuracy;

[0074] The trained multi-head attention-bidirectional GRU model is used to predict the changing trends of gas concentration and flow in the future.

[0075] The zoned drainage warning and emergency response module is used to activate the warning mechanism when the real-time drainage parameters, the real-time operating status of the drainage pump station, or the real-time operating status of each drainage zone meet the warning conditions, including:

[0076] Set the warning parameter threshold:

[0077] Zone extraction concentration warning: Set the threshold range of normal extraction concentration. When the gas concentration in the extraction pipeline of the extraction zone monitored in real time exceeds this range, the system automatically activates the warning mechanism;

[0078] Zoned drainage negative pressure warning: When multiple threshold ranges of normal drainage negative pressure are set, the system automatically activates the warning mechanism when the real-time monitored drainage negative pressure of the drainage zone exceeds this range;

[0079] Zone temperature warning: Set the normal extraction temperature. When the real-time monitored extraction zone temperature exceeds this range, the system automatically activates the warning mechanism;

[0080] CO concentration exceeding the limit warning: Install a CO sensor in the extraction pipeline and set a safety threshold for CO concentration. When the CO concentration in the extraction pipeline of a certain extraction zone exceeds the set threshold in real time monitoring, the system will issue an alarm;

[0081] Cumulative Pure Gas Volume Warning: Set a predetermined value for the cumulative pure gas volume in a zone. When the extracted gas volume reaches this predetermined value, the system will automatically issue a warning to stop extraction.

[0082] Pumping station abnormal operation warning: Set normal operating ranges for the pumping station's operating parameters and the multiple pumping zones it includes. When parameters exceed the normal range, the system will issue a warning.

[0083] The above parameters are automatically monitored by the gas extraction monitoring system. Once the monitored parameters meet the warning conditions, the system will automatically issue an audible and visual alarm to remind staff to take corresponding measures.

[0084] Emergency response measures:

[0085] Pumping stop operation: When the CO concentration exceeds the standard or the accumulated pure gas volume reaches a predetermined value, the system will automatically stop pumping to ensure safety. For abnormal operation of the pumping station or the multiple pumping zones included in the pumping station, the system will perform fault diagnosis and prompt necessary maintenance and repairs.

[0086] By combining multi-head attention and bidirectional GRU, the model not only captures the complex temporal characteristics of gas concentration changes, but also reduces fluctuations during extraction through intelligent control and early warning emergency response, thereby improving the stability and safety of the entire gas extraction system. This approach is particularly suitable for coal mine gas extraction systems, which have complex dynamic characteristics and multivariate interactions.

[0087] like Figure 2 As shown, the present application provides an intelligent control method for coordinated extraction of coal mine gas by zones, including:

[0088] S201, collecting real-time drainage parameters of each area underground and real-time operation data of the drainage pump station based on the zoned drainage sensor module;

[0089] S202: Based on the characteristics of the zoned drainage sensor modules and the characteristics of the real-time drainage parameters, including gas concentration data, gas flow data, drainage negative pressure data, carbon monoxide concentration data, and temperature data, the real-time drainage parameters of each area in the well and the operation data of the drainage pump station are classified and processed to obtain classification results;

[0090] S203, adjusting the real-time operating status of the drainage pump station and the real-time operating status of each drainage zone according to the classification processing result; wherein the drainage pump station includes multiple drainage zones;

[0091] S204: When the real-time extraction parameters, the real-time operating status of the extraction pump station, or the real-time operating status of each extraction zone meets the early warning conditions, the early warning mechanism is activated.

[0092] It should be noted that in this application, the embodiments implemented by the intelligent control system for coordinated extraction of coal mine gas from different zones can be referenced with the embodiments implemented by the intelligent control method for coordinated extraction of coal mine gas from different zones, and this application will not go into details one by one.

[0093] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent control system for coordinated extraction of coal mine gas by zones, characterized in that: include: The zoned extraction sensor module is used to collect real-time extraction parameters of each area underground and real-time operation data of the extraction pump station; A zoned gas extraction parameter classification and processing module is used to classify and process the real-time extraction parameters of each area of ​​the well and the operating data of the extraction pump station according to the characteristics of the zoned extraction sensor module and the characteristics of the real-time extraction parameters, including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data, and temperature data, to obtain a classification processing result; The sub-area gas extraction parameter classification and processing module classifies and processes the real-time extraction parameters of each area of ​​the well and the operating data of the extraction pump station according to the characteristics of the sub-area extraction sensor module and the characteristics of the real-time extraction parameters including gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data, and temperature data, and obtains classification processing results. The module is configured as follows: Determining the strength of correlation between the deployment position of each sensor included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameter; Creating an association matrix; wherein the rows of the association matrix represent the deployment locations of the sensors included in the partitioned sampling sensor module, and the columns of the association matrix represent the characteristics of the data included in the real-time sampling parameters; and the elements in the association matrix represent the strength of association between the deployment locations of the sensors included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameters; For each deployment location of the sensor included in the partitioned sampling sensor module, the characteristics of the data included in the real-time sampling parameters corresponding to the deployment location of the sensor in the association matrix are processed as transactions to construct an FP tree; wherein each node of the FP tree represents a characteristic, and the weight of the node represents the frequency of occurrence of the characteristic in the transaction; By constructing the obtained FP tree, mining the feature combinations that appear commonly in the deployment locations of multiple sensors; Based on a combination of characteristics that commonly appear in the deployment locations of multiple sensors, the same extraction mode is assigned to the extraction partitions having the common characteristics; For each feature combination, a conditional pattern base is constructed; wherein the conditional pattern base is based on a given feature combination and mines frequent combinations of other features; matching the real-time drainage parameters of each area of ​​the well and the operating data of the drainage pump station with the frequent combinations of other characteristics mined and the characteristic combinations, and classifying the real-time drainage parameters of each area of ​​the well and the operating data of the drainage pump station according to the matching results; A zoned drainage intelligent control module is used to adjust the real-time operating status of the drainage pump station and the real-time operating status of each drainage zone according to the classification processing result; wherein the drainage pump station includes multiple drainage zones; Determining the correlation strength between the deployment location of each sensor included in the partitioned sampling sensor module and the characteristics of the data included in the real-time sampling parameters is configured as follows: Preprocessing historical coal mine gas extraction monitoring data to handle outliers and missing values, including treating outliers as missing values ​​and applying sliding Lagrangian interpolation to interpolate the missing values; wherein the historical coal mine gas extraction monitoring data includes extracted gas concentration and flow data; For each sensor deployment location, an ARIMA model is constructed to analyze the time series characteristics of the extracted gas concentration and flow rate; Applying a sequential learning algorithm, adjusting the model parameters of the ARIMA model according to changes in drainage gas concentration, drainage volume attenuation patterns, abnormal carbon monoxide concentrations, and changes in drainage negative pressure; Determining, based on the adjusted ARIMA model, the strength of association between the deployment location of each sensor and the characteristics of the data included in the real-time sampling parameters; The zoned extraction warning and emergency response module is used to activate the warning mechanism when the real-time extraction parameters, the real-time operating status of the extraction pump station or the real-time operating status of each extraction zone meet the warning conditions.

2. The intelligent control system for coordinated extraction of coal mine gas by zones according to claim 1 is characterized in that: The characteristics of the data included in the real-time extraction parameters include changes in the extraction gas concentration, the law of extraction volume attenuation, abnormal carbon monoxide concentration, and changes in the extraction negative pressure.

3. The intelligent control system for coal mine gas extraction according to claim 1, characterized in that: By constructing the obtained FP tree, feature combinations that commonly appear in the deployment locations of multiple sensors are mined.

4. The intelligent control system for coordinated extraction of coal mine gas by zones according to claim 1 is characterized in that: For each feature combination, a conditional pattern base is constructed and configured as follows: Extracting a feature combination that meets a preset minimum support threshold from the FP tree using the FP-Growth algorithm; For each feature combination that meets a preset minimum support threshold, a conditional FP tree is constructed; wherein the conditional FP tree is a subtree of the FP tree, including paths related to the feature combination that meets the preset minimum support threshold.

5. The intelligent control system for coordinated extraction of coal mine gas by zones according to claim 4 is characterized in that: For each feature combination, a conditional pattern base is constructed and configured as follows: Based on the conditional FP tree, a candidate item set is generated; wherein the candidate item set includes all other features that can appear together with the frequent feature combination that meets the preset minimum support threshold; For each item set in the candidate item set, calculate its conditional support; wherein the conditional support is the frequency of occurrence of the item set in the candidate item set under the condition of a given frequent feature combination; According to a preset minimum support threshold, filter out item sets whose conditional support is higher than the preset minimum support threshold; wherein the item sets whose conditional support is higher than the threshold constitute the frequent item sets in the conditional pattern base; Add the filtered frequent itemsets to the conditional pattern base.

6. The intelligent control system for coordinated extraction of coal mine gas by zones according to claim 1 is characterized in that: The zoned drainage intelligent control module adjusts the real-time operating status of the drainage pump station according to the classification processing results, and is configured as follows: Receiving the classification and processing result outputted from the sub-area gas extraction parameter classification and processing module; Set control objectives and operational constraints for gas extraction according to coal mine safety standards and operating procedures; Construct a model to describe the relationship between the operating status of the drainage pump station and the gas drainage parameters; Based on the model used to describe the relationship between the operating status of the extraction pump station and the gas extraction parameters and the classification processing results, predict the changing trend of gas concentration and flow rate in the future period; Defining optimization objectives; wherein the optimization objectives include the objectives of maximum deviation of gas concentration and flow rate and the objective of minimizing energy consumption of the pumping station; Calculating optimal operating parameters of the extraction pump station based on the predicted changing trends of gas concentration and flow and the solution of the optimization objective; An adjustment strategy is implemented according to the operating parameters of the optimal extraction pump station and the operating constraints; wherein the adjustment strategy includes a negative pressure adjustment strategy for the extraction partition, a pumping stop decision strategy, and a pump station operating status adjustment strategy.

7. The intelligent control system for coordinated extraction of coal mine gas by zones according to claim 1 is characterized in that: The operation data of the extraction pump station includes extraction pump operation rate data, valve opening and closing position data, flow regulation parameter data, pressure adjustment parameter data and pipeline valve opening degree data.

8. A method for intelligent control of coal mine gas zone coordinated extraction, characterized in that: The intelligent control system for coordinated zoning extraction of coal mine gas according to claim 1 comprises: Based on the zoned extraction sensor module, real-time extraction parameters of each area underground and real-time operation data of the extraction pump station are collected; Based on the characteristics of the zoned drainage sensor module and the characteristics of the real-time drainage parameters including gas concentration data, gas flow data, drainage negative pressure data, carbon monoxide concentration data, and temperature data, the real-time drainage parameters of each area of ​​the well and the operating data of the drainage pump station are classified and processed to obtain a classification processing result; According to the classification processing result, adjusting the real-time operating status of the extraction pump station and the real-time operating status of each extraction zone; wherein the extraction pump station includes multiple extraction zones; When the real-time extraction parameters, the real-time operating status of the extraction pump station, or the real-time operating status of each extraction zone meets the early warning condition, the early warning mechanism is activated.

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