Intelligent regulation and control system and regulation and control method for coal mine gas zoning collaborative extraction

Abstract

The invention discloses an intelligent regulation and control system and method for coal mine gas partition collaborative extraction, and relates to the technical field of coal mines, and the system comprises a partition extraction sensor module which is used for collecting real-time extraction parameters of all underground areas and real-time operation data of an extraction pump station; the partition gas extraction parameter classification and processing module is used for performing classification processing on the real-time extraction parameters of each underground area and the operation data of the extraction pump station according to the characteristics of the partition gas extraction sensor module and the characteristics of the data included in the real-time extraction parameters to obtain a classification processing result; the partition extraction intelligent regulation and control module is used for adjusting the real-time operation state of an extraction pump station and the real-time operation state of each extraction partition according to the classification processing result; and the subarea extraction early warning and emergency response module is used for starting an early warning mechanism when the real-time extraction parameters, the real-time operation state of the extraction pump station or the real-time operation state of each extraction subarea meet the early warning condition.

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 gas extraction in coal mine zones. Background Art

[0002] Coal mine gas extraction is a key link in coal mine safety production. Its main purpose is to extract gas from coal seams to the ground through specific technical means to eliminate the risk of coal seam outbursts, reduce the amount of coal seam gas in the mine, avoid gas explosion accidents, and ensure the safety of miners. At the same time, gas can also be used as an energy source. At present, coal mine gas extraction technology mainly includes coal seam in-line hole extraction, through-layer drilling extraction, high-position fissure drilling extraction, and goaf gas extraction. For gas gushing out of the roadway, ventilation is used for dilution.

[0003] The gas extraction system in the prior art can automatically adjust the valve according to the gas extraction concentration of each borehole (or each group of connected boreholes). Specifically, when the sensor detects that the gas concentration is low, the system will automatically close the corresponding extraction valve to save resources; when the concentration is high, the system will automatically open the valve for 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 holes) 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 solution has been proposed yet. Summary of the invention

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

[0007] In the first aspect, the present application provides a coal mine gas zone coordinated extraction intelligent control system, including: a zone 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 zone 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 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 a classification processing result; a zone 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 zone 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] In 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 operating data of the extraction pump station based on a zone extraction sensor module; classifying the real-time extraction parameters of each area underground and the operating 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 a classification processing result; adjusting 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; 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, the warning mechanism is activated.

[0009] Based on the embodiments provided in the present application, based on the zoned extraction sensor module, the zoned extraction gas parameter classification and processing module, the zoned extraction intelligent control module and the zoned extraction early warning and emergency response module, the real-time extraction parameters of different areas are collected through the zoned extraction sensor module, thereby realizing the coordinated management of gas extraction in various areas underground and improving the pertinence and efficiency of extraction; the zoned 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, thereby improving the scientificity and accuracy of the control; the zoned extraction intelligent control module can adjust the extraction pump in real time according to the classification processing results. The operation status of the station and the real-time operation status of each extraction zone make the extraction operation more flexible and responsive; the zone extraction warning and emergency response module can quickly start the warning mechanism when the warning conditions are met, which improves the ability to respond to emergencies and enhances the safety of the mine; the real-time operation status of the extraction pump station and the real-time operation 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 and support 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 the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

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

[0012] Figure 2 The present invention is a flowchart of an optional intelligent control system for coordinated extraction of coal mine gas in different zones according to an embodiment of the present application.

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

[0014] In order to enable those 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, if Figure 1 As shown, the present application provides a coal mine gas zoning coordinated extraction intelligent control system, including:

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

[0017] The sub-area extraction gas 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 operation 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 obtain the classification processing results;

[0018] The zoned extraction intelligent control module 103 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;

[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 extraction pump station includes a plurality of extraction zones, each of which has a plurality of corresponding groups of sensors deployed therein. For example, extraction zone 1 is deployed with group 1 sensors, and 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 when gas extraction starts and the number of holes drilled in each extraction zone are usually different, it is necessary to handle each extraction zone according to the situation.

[0023] Based on the embodiments provided in the present application, based on the zoned extraction sensor module, the zoned extraction gas parameter classification and processing module, the zoned extraction intelligent control module and the zoned extraction early warning and emergency response module, the real-time extraction parameters of different areas are collected through the zoned extraction sensor module, thereby realizing the coordinated management of gas extraction in various areas underground and improving the pertinence and efficiency of extraction; the zoned 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, thereby improving the scientificity and accuracy of the control; the zoned extraction intelligent control module can adjust the extraction in real time according to the classification processing results. The operating status of the pump station and the real-time operating status of each extraction zone make the extraction operation 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 the gas extraction situation and support 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 partitioned gas extraction parameter classification and processing module classifies and processes the real-time extraction parameters of each area in the well and the operation data of the extraction pump station according to the characteristics of the partitioned 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 the classification processing results, which are configured as follows:

[0025] Determine 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] Create an association matrix; wherein the rows of the association matrix represent the deployment positions of the sensors included in the partition extraction sensor module, and the columns of the association matrix represent the characteristics of the data included in the real-time extraction parameters; the elements in the association matrix represent the strength of association between the deployment positions of the sensors included in the partition extraction sensor module and the characteristics of the data included in the real-time extraction parameters;

[0027] For each deployment position 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 position 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;

[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 appear in common at 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; wherein 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 mined characteristics, 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 precursor to 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 deployment location m of the sensor; 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 occurrence of characteristic n in the deployment position m of the sensor to the total frequency of occurrence of all characteristics in the deployment position m of the sensor.

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

[0042] Preprocess the 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 extraction 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] The sequential learning algorithm is applied to adjust the model parameters of the ARI MA model according to the changes in the extraction gas concentration, the attenuation law of the extraction volume, the abnormal carbon monoxide concentration, and the changes in the 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] The FP-Growth algorithm is used to extract the feature combination that meets 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 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:

[0057] Receive the classification and processing results output from the partition extraction gas 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, the control objectives of 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 extraction pump station and the gas extraction 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 in the future period is predicted;

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

[0063] Among them, for 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 means 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 trend of gas concentration and flow and the solution of the optimization target;

[0065] The adjustment strategy is implemented according to the optimal operating parameters and operating constraints of the extraction pump station; among them, the adjustment strategy includes the negative pressure adjustment strategy of 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 expected 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 and classification processing 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 in the future 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 dependencies between time series data, which enables the model to consider both historical and future information and enhance the ability to predict gas concentration and flow rate trends; Multi-head attention layer: based on the bidirectional GRU, 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 back-propagation algorithm, and improve the prediction accuracy of the model;

[0074] The trained multi-head attention-bidirectional GRU model is used to predict the changing trend 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. When the real-time monitored drainage negative pressure of the drainage zone exceeds this range, the system automatically activates the warning mechanism;

[0079] Partition temperature warning: Set the normal extraction temperature. When the temperature of the extraction partition monitored in real time exceeds this range, the system automatically activates the warning mechanism;

[0080] CO concentration exceeding the standard 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 a warning;

[0081] Cumulative pure gas volume warning: Set the preset value of the cumulative pure gas volume in the partition. When the extracted gas volume reaches this preset value, the system will automatically issue a stop extraction warning.

[0082] Pumping station abnormal operation warning: Set the normal operation range for the pumping station operation parameters and the multiple pumping zones included in the pumping station. When the 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 the preset value, the system will automatically stop pumping to ensure safety; for abnormal operation of the pumping station or multiple pumping zones included in the pumping station, the system will perform fault diagnosis and prompt necessary maintenance and repair.

[0086] By combining multi-head attention and bidirectional GRU, the model can not only capture the complex time series characteristics of gas concentration changes, but also reduce fluctuations in the extraction process through intelligent regulation and early warning emergency response, thereby improving the stability and safety of the entire gas extraction system. This method is particularly suitable for coal mine gas extraction systems with complex dynamic characteristics and multi-variable interactions.

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

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

[0089] S202, classifying and processing the real-time extraction parameters of each area in the well 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, and obtaining the classification processing results;

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

[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 meet the early warning conditions, the early warning mechanism is activated.

[0092] It should be noted that in the present application, the embodiments implemented by the intelligent control system for coordinated extraction of coal mine gas by zoning can be cross-referenced with the embodiments implemented by the intelligent control method for coordinated extraction of coal mine gas by zoning, and the present 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 specification 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 different 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 extraction gas parameter classification and processing module is used to classify and process the real-time extraction parameters of each area of ​​the well and the operation 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, and obtain a classification processing result; A zoned extraction intelligent control module, 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; 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 different zones according to claim 1 is characterized in that: The sub-area extraction gas parameter classification and processing module classifies and processes the real-time extraction parameters of each area of ​​the well and the operation data of the extraction pump station according to the characteristics of the sub-area extraction sensor module and the characteristics of the gas concentration data, gas flow data, extraction negative pressure data, carbon monoxide concentration data and temperature data included in the real-time extraction parameters, and obtains the classification processing results, and is configured as follows: Determining the strength of association between the deployment positions of the sensors included in the partitioned extraction sensor module and the characteristics of the data included in the real-time extraction parameters; Create an association matrix; wherein the rows of the association matrix represent the deployment positions of the sensors included in the partitioned extraction sensor module, and the columns of the association matrix represent the characteristics of the data included in the real-time extraction parameters; the elements in the association matrix represent the strength of association between the deployment positions of the sensors included in the partitioned extraction sensor module and the characteristics of the data included in the real-time extraction parameters; For the deployment position of each sensor included in the partitioned sampling sensor module, the characteristics of the data included in the real-time sampling parameters corresponding to the deployment position 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 in common in the deployment locations of multiple sensors; Based on a combination of characteristics that appear in common at 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; The real-time extraction parameters of each area in the well and the operating data of the extraction pump station are matched with the frequent combinations of other excavated characteristics and the characteristic combinations, and the real-time extraction parameters of each area in the well and the operating data of the extraction pump station are classified and processed according to the matching results.

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

4. The intelligent control system for coal mine gas extraction according to claim 2 is characterized in that: By constructing the obtained FP tree, feature combinations that commonly appear in the deployment locations of multiple sensors are mined.

5. The intelligent control system for coordinated extraction of coal mine gas by different zones according to claim 3 is characterized in that: The determining of the correlation strength between the deployment position of each sensor included in the partitioned extraction sensor module and the characteristics of the data included in the real-time extraction parameter is configured as follows: Preprocessing the historical coal mine gas extraction monitoring data to process outliers and missing values, including: treating outliers as missing values ​​and applying sliding Lagrange interpolation to interpolate the missing values; wherein the historical coal mine gas extraction monitoring data includes extraction 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 extraction gas concentration, extraction volume attenuation law, abnormal carbon monoxide concentration, and changes in extraction negative pressure; According to 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 is determined.

6. The intelligent control system for coordinated extraction of coal mine gas by different zones according to claim 5 is characterized in that: For each feature combination, a conditional pattern base is constructed, which is configured as follows: Extracting a feature combination that meets a preset minimum support threshold from the FP tree using an 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.

7. The intelligent control system for coordinated extraction of coal mine gas by different zones according to claim 6 is characterized in that: For each feature combination, a conditional pattern base is constructed, which is 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.

8. The intelligent control system for coordinated extraction of coal mine gas by different zones according to claim 2 is characterized in that: The zoned extraction intelligent control module adjusts the real-time operation status of the extraction pump station according to the classification processing result, 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 extraction pump station and the gas extraction 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 change trend of gas concentration and flow rate in the future period of time; 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 pump station; Calculating the optimal operating parameters of the extraction pump station according to the predicted changing trends of gas concentration and flow rate and the solution of the optimization target; An adjustment strategy is implemented according to the optimal operating parameters of the 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.

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

10. A method for intelligent control of coal mine gas zoning coordinated extraction, characterized in that: include: Based on the zoned extraction sensor module, the real-time extraction parameters of each area underground and the real-time operation data of the extraction pump station are collected; 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, the real-time extraction parameters of each area of ​​the underground well and the operation data of the extraction pump station are classified and processed to obtain a classification processing result; According to the classification processing result, adjusting the real-time operation status of the extraction pump station and the real-time operation status of each extraction partition; wherein the extraction pump station includes a plurality of extraction partitions; 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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