Coal mine drilling oriented whole-process gas monitoring and prevention method, device and equipment

Abstract

This disclosure presents a method, apparatus, and equipment for full-process gas monitoring and control in coal mine drilling. One specific implementation of the method includes: constructing real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters; calibrating the real-time drilling rig construction characteristics to generate construction characteristic baseline information; detecting gas vents in the real-time drilling rig construction characteristics to generate a gas vent control strategy, and executing the gas vent control operation corresponding to the gas vent control strategy; monitoring gas anomalies in the gas characteristic sequence to generate gas anomaly classification information; generating a gas monitoring and control strategy based on the gas vent control strategy and the gas anomaly classification information, and sending the gas monitoring and control strategy to a monitoring terminal. This implementation can perform full-process gas monitoring and control in coal mine drilling operations, thereby improving the stability of gas anomaly monitoring and enhancing drilling operation safety.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the fields of computer technology and gas monitoring and control, specifically to a method, apparatus, and equipment for full-process gas monitoring and control in coal mine boreholes. Background Technology

[0002] Gas monitoring and control is a crucial aspect of coal mine drilling operations, encompassing the entire process from drilling and extraction to operation in goaf or confined areas. Currently, gas monitoring and control in coal mine drilling operations typically involves different monitoring methods for each stage of the operation. For example, during the drilling construction stage, the focus is on blowout prevention and control; after drilling is completed, extraction parameters are monitored; and in goaf or confined areas, marker gases are detected through sampling or fixed monitoring points.

[0003] However, the following technical problems exist when using the above methods for gas monitoring and control: When a single-stage gas monitoring method is used, the monitoring parameters only reflect the instantaneous state of the current stage, making it difficult to demonstrate the continuous changes in gas release and gas evolution between different operational stages. Furthermore, within a single stage, gas parameters are easily affected by construction conditions, extraction conditions, or environmental factors. Relying solely on local parameter changes makes it difficult to accurately distinguish between normal fluctuations and abnormal gas changes, resulting in poor gas monitoring stability and thus reducing the safety of coal mine drilling operations. Summary of the Invention

[0004] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] Some embodiments of this disclosure propose a method, apparatus, and equipment for full-process gas monitoring and control in coal mine boreholes to address the technical problems mentioned in the background section above.

[0006] In a first aspect, some embodiments of this disclosure provide a method for full-process gas monitoring and control in coal mine boreholes. The method includes: constructing real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters, wherein the drilling rig operation parameters are collected from drilling equipment currently drilling in a coal mine; calibrating the real-time drilling rig construction characteristics to a baseline state based on historical drilling rig construction characteristic sequences to generate construction characteristic baseline information, wherein the construction characteristic baseline information characterizes the baseline construction state of the drilling equipment within the coal mine borehole at the current construction progress; and calibrating the real-time drilling rig... The system detects gas eruptions based on construction characteristics to generate gas eruption prevention and control strategies, and executes the corresponding gas eruption prevention and control operations. It also monitors gas anomalies in the constructed gas characteristic sequence to generate various gas anomaly classification information. These gas characteristic sequences are generated based on gas parameter sequences periodically extracted from completed coal mine boreholes and connected goaf or sealed areas. Finally, based on the gas eruption prevention and control strategies and the gas anomaly classification information, a gas monitoring and control strategy is generated and sent to the monitoring terminal.

[0007] Secondly, some embodiments of this disclosure provide a full-process gas monitoring and control device for coal mine boreholes. The device includes: a construction unit configured to construct real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters, wherein the drilling rig operation parameters are collected from the drilling equipment currently drilling in the coal mine; a reference state calibration unit configured to perform reference state calibration on the real-time drilling rig construction characteristics based on a historical drilling rig construction characteristic sequence to generate construction characteristic reference information, wherein the construction characteristic reference information characterizes the reference construction state of the drilling equipment in the coal mine borehole at the current construction progress; and a gas eruption detection unit configured to detect gas eruptions based on the construction characteristic reference information. The aforementioned real-time drilling rig construction characteristics are used to detect gas eruptions, thereby generating a gas eruption prevention and control strategy, and executing the gas eruption prevention and control operations corresponding to the aforementioned gas eruption prevention and control strategy; a gas anomaly monitoring unit is configured to monitor gas anomalies in the constructed gas feature sequence to generate various gas anomaly classification information, wherein the aforementioned gas feature sequence is generated based on the gas gas parameter sequence periodically extracted from coal mine boreholes that have completed drilling construction and the connected goaf or closed area; a generation unit is configured to generate a gas monitoring and control strategy based on the aforementioned gas eruption prevention and control strategy and the aforementioned various gas anomaly classification information, and send the aforementioned gas monitoring and control strategy to the monitoring terminal.

[0008] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0009] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0010] The above-described embodiments of this disclosure have the following beneficial effects: the full-process gas monitoring and control method for coal mine drilling, as described in some embodiments of this disclosure, can perform full-process gas monitoring and control of coal mine drilling operations, thereby improving the stability of gas anomaly monitoring and thus enhancing drilling operation safety. Specifically, the reason for poor gas monitoring stability and reduced safety in coal mine drilling operations is that when a single-stage gas monitoring method is used, the monitoring parameters only reflect the instantaneous state of the current stage, making it difficult to reflect the continuous changes in gas release and gas evolution between different operation stages. Furthermore, within a single stage, gas parameters are easily affected by construction conditions, extraction conditions, or environmental factors. Relying solely on local parameter changes makes it difficult to accurately distinguish between normal fluctuations and abnormal gas changes, resulting in poor gas monitoring stability and thus reducing the safety of coal mine drilling operations. Based on this, the full-process gas monitoring and control method for coal mine drilling, as described in some embodiments of this disclosure, firstly constructs real-time drilling rig construction characteristics based on the real-time collected drilling rig operation parameters. These drilling rig operation parameters are collected from the drilling equipment currently performing drilling operations in the coal mine. Therefore, by collecting drilling rig operating parameters in real time and constructing construction features, the operating status of the drilling equipment during drilling can be reflected, thereby quantifying the disturbance of the coal seam and the potential gas release status during the drilling stage. Then, based on the historical drilling rig construction feature sequence, the aforementioned real-time drilling rig construction features are calibrated to a baseline state to generate construction feature baseline information. This baseline information represents the baseline construction state of the drilling equipment within the coal mine borehole at the current construction progress. Thus, by calibrating the real-time construction features against historical construction features, the construction features of the same borehole at different construction progresses are comparable, reducing interference from normal fluctuations caused by differences in coal seam conditions, abnormally slow gas rise, or differences in borehole depth, and improving the stability of identifying abnormal construction states. Subsequently, based on the aforementioned construction feature baseline information, gas eruption detection is performed on the aforementioned real-time drilling rig construction features to generate a gas eruption prevention and control strategy, and to execute the corresponding gas eruption prevention and control operations. Therefore, by detecting blowouts based on the deviation of construction characteristics from the baseline state, the assessment of blowout risk can be made independent of absolute thresholds, but rather correlated with the actual construction status of the current borehole. This allows for more timely and stable identification of blowout risks caused by gas release triggered by borehole construction, and enables the implementation of corresponding prevention and control measures in advance. Secondly, gas anomaly monitoring is performed on the constructed gas characteristic sequence to generate various gas anomaly classification information. The aforementioned gas characteristic sequence is generated based on the gas parameter sequence of periodically extracted from completed coal mine boreholes and connected goafs or confined areas.Therefore, by continuously monitoring gas parameters during the extraction phase after drilling, abnormal changes in parameters such as gas concentration and extraction flow rate can be identified holistically, rather than relying on the instantaneous concentration of a single parameter at a single moment. This improves the accuracy and stability of gas anomaly detection. Finally, based on the gas eruption control strategy and the gas anomaly classification information, a gas monitoring and control strategy is generated and sent to the monitoring terminal. Thus, by integrating the eruption control strategy during the drilling construction phase with the gas anomaly classification information during the extraction phase, the gas control strategy can simultaneously reflect the eruption risk during the construction phase and the continuous changes in gas during the extraction phase. This reduces monitoring errors or omissions caused by monitoring in a single phase, improves the stability and continuity of gas anomaly identification, and ultimately enhances the overall safety of coal mine drilling operations. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0012] Figure 1 This is a flowchart of some embodiments of the full-process gas monitoring and control method for coal mine boreholes according to this disclosure; Figure 2 This is a schematic diagram of the benchmark state calibration in the fully-process gas monitoring and control method for coal mine boreholes disclosed herein; Figure 3 This is a schematic diagram of gas anomaly monitoring in the full-process gas monitoring and control method for coal mine boreholes disclosed herein; Figure 4 This is a structural schematic diagram of some embodiments of the full-process gas monitoring and control device for coal mine boreholes according to the present disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0016] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0018] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Figure 1 A process 100 of some embodiments of the full-process gas monitoring and control method for coal mine boreholes according to the present disclosure is shown. This full-process gas monitoring and control method for coal mine boreholes includes the following steps: Step 101: Construct real-time drilling rig construction features based on the real-time collected drilling rig operation parameters.

[0020] In some embodiments, the implementing entity (e.g., a computing device) of the full-process gas monitoring and control method for coal mine boreholes can construct real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters. These drilling rig operation parameters are collected from the drilling equipment currently performing borehole construction in the coal mine. The drilling equipment can refer to a complete set of drilling equipment used for borehole construction underground or above ground in a coal mine. The drilling rig operation parameters can include acquisition time, drilling rig torque, drilling rig thrust, drilling rig spindle speed, and drilling depth. The drilling rig torque can be the rotational torque output by the drilling rig spindle when rotating the drill bit, used to overcome the resistance of the coal or rock mass to the rotation of the drill bit, and can be measured by a torque sensor arranged on the spindle or drive shaft. The drilling rig thrust can be the thrust applied to the drill bit along the borehole axis during drilling, used to continuously advance the drill bit into the coal body, and can be measured by a pressure sensor or tension sensor arranged on the propulsion mechanism or slide. The aforementioned spindle speed can refer to the number of rotations of the spindle per unit time, used to characterize the rotational cutting speed of the drill bit, and can be measured by a speed sensor. The aforementioned drilling depth can refer to the axial advance distance of the drill bit relative to the starting position of the borehole, used to characterize the drilling progress, and can be measured by a displacement sensor arranged on the advance carriage. In practice, firstly, the aforementioned execution entity can control the associated sensors to periodically collect the drilling operation parameters of the aforementioned drilling equipment according to a fixed acquisition cycle (e.g., 5 minutes) via wireless or wired connection. Then, in response to determining that there are no missing values ​​in the aforementioned drilling operation parameters, the aforementioned execution entity can stitch together the various parameters included in the aforementioned drilling operation parameters into real-time drilling construction characteristics.

[0021] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0022] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here. It should be understood that the number of the aforementioned computing devices can be arbitrary, depending on the implementation requirements.

[0023] In some optional implementations of certain embodiments, the aforementioned execution entity can construct real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters through the following steps: The first step is to generate the drilling footage rate based on the drilling depths included in the aforementioned drilling rig operating parameters and the drilling depths included in historical drilling rig operating parameters. The historical drilling rig operating parameters can be those collected in the previous data collection cycle for the same coal mine borehole. The drilling footage rate refers to the distance the drill bit advances per unit time, reflecting drilling efficiency and changes in borehole resistance. In practice, the executing entity can determine the depth difference between the drilling depths included in the aforementioned drilling rig operating parameters and the drilling depths included in each of the historical drilling rig operating parameters, and the time difference between the respective data collection times. The ratio of the depth difference to the time difference is then determined as the drilling footage rate. The default unit is m / s.

[0024] The second step is to generate the drilling resistance per unit footage based on the drilling rig operating parameters, including the drilling rig torque and drilling rig thrust. This drilling resistance per unit footage refers to the overall resistance that the drill bit must overcome to advance one unit distance during drilling operations. For example, a significant increase in drilling resistance per unit footage within a short period may indicate a change in the drilling environment, possibly accompanied by abrupt changes in coal seam structure or disturbances caused by gas release. In practice, the implementing entity can first determine the drilling resistance per unit footage by taking the weighted sum of the ratios between the drilling rig torque and the determined drilling speed, i.e., drilling resistance per unit footage = a × (drilling rig torque / drilling speed) + b × (drilling rig torque / drilling speed). Here, a and b can both be 0.5.

[0025] The third step is to generate the rate of change of drilling resistance per unit footage based on the generated drilling resistance per unit footage. The rate of change of drilling resistance per unit footage refers to the speed at which the drilling resistance per unit footage changes between adjacent data acquisition cycles, used to describe the dynamic changes in resistance during drilling. For example, a continuously increasing or rapidly fluctuating rate of change of drilling resistance can indicate unstable drilling conditions, potentially leading to abnormal situations such as changes in coal seam structure or rapid gas release. In practice, the executing entity can determine the rate of change of drilling resistance as the ratio of the difference between the historical drilling resistance per unit footage and the current drilling resistance per unit footage, to the corresponding time difference. The historical drilling resistance per unit footage can be the drilling resistance per unit footage determined during the last drilling parameter acquisition.

[0026] The fourth step is to splice together the above-mentioned drilling rig operation parameters, including acquisition time, drilling depth, drilling rig rotation speed, drilling rig advance speed, drilling rig unit advance resistance, and the rate of change of advance resistance, to obtain real-time drilling rig construction characteristics.

[0027] Step 102: Based on the historical drilling rig construction characteristic sequence, perform benchmark state calibration on the above-mentioned real-time drilling rig construction characteristics to generate construction characteristic benchmark information.

[0028] In some embodiments, the executing entity can perform benchmark state calibration on the real-time drilling rig construction features based on the historical drilling rig construction feature sequence to generate construction feature benchmark information. This construction feature benchmark information characterizes the benchmark construction state of the drilling rig equipment within the coal mine borehole at the current construction progress. In practice, firstly, the executing entity can generate the mean features of each historical drilling rig construction feature included in the historical drilling rig construction feature sequence and the real-time drilling rig construction features. The mean value of the drilling rig unit footage resistance and the mean value of the rate of change of footage resistance included in the mean features are determined as the construction feature benchmark information. Then, the executing entity can generate the variance between each historical drilling rig construction feature included in the historical drilling rig construction feature sequence and the mean value of each drilling rig unit footage resistance included in the real-time drilling rig construction features, and the variance between each rate of change of footage resistance. Finally, the executing entity can determine the mean value of the drilling rig unit footage resistance, the mean value of the rate of change of footage resistance included in the mean features, the variance between the determined mean value of the drilling rig unit footage resistance, and the variance between each rate of change of footage resistance as the construction feature benchmark information.

[0029] In some optional implementations of certain embodiments, the aforementioned execution entity may perform benchmark state calibration on the real-time drilling rig construction features based on the historical drilling rig construction feature sequence through the following steps to generate construction feature benchmark information: The first step is to generate initial feature benchmark information based on the first and second historical drilling rig construction features in the aforementioned historical drilling rig construction feature sequence. This initial feature benchmark information includes: initial average resistance per unit footage, initial variance of resistance per unit footage, initial average rate of change of resistance per unit footage, and variance of the initial rate of change of resistance per unit footage. The first and second historical drilling rig construction features are, respectively, the first and second historical drilling rig construction features in the aforementioned historical drilling rig construction feature sequence. In practice, the executing entity can generate the mean, variance, average rate of change of resistance per unit footage, and variance of the initial average resistance per unit footage, initial variance of resistance per unit footage, initial average rate of change of resistance per unit footage, and initial variance of the rate of change of resistance per unit footage, respectively, as the initial average resistance per unit footage, initial variance of resistance per unit footage, initial average rate of change of resistance per unit footage, and initial variance of the rate of change of resistance per unit footage, thus obtaining the initial feature benchmark information.

[0030] The second step involves calibrating the initial feature baseline information based on the historical drilling rig construction features that meet the positional conditions in the aforementioned historical drilling rig construction feature sequence, thereby updating the initial feature baseline information. The aforementioned positional conditions can be that the position of the historical drilling rig construction feature that meets the conditions is greater than or equal to 2.

[0031] In some optional implementations of certain embodiments, the aforementioned execution entity may update the initial feature reference information by performing historical state calibration on each historical drilling rig construction feature that satisfies the positional condition in the aforementioned historical drilling rig construction feature sequence through the following steps: The first step is to perform the following state calibration steps for every two historical drilling rig construction features that meet the positional conditions in the above historical drilling rig construction feature sequence, based on the initial feature reference information: The first sub-step generates, based on the two historical drilling rig construction characteristics mentioned above, the average resistance per unit footage, the variance of resistance per unit footage, the average rate of change of resistance per unit footage, and the variance of the rate of change of resistance per unit footage. In practice, the aforementioned execution entity can generate the average resistance per unit footage (i.e., the average of the resistance per unit footage of the two drilling rigs), the variance of resistance per unit footage (i.e., the variance between the resistance per unit footage of the two drilling rigs), the average rate of change of resistance per unit footage (i.e., the average of the rates of change of resistance per unit footage of the two drilling rigs), and the variance of the rate of change of resistance per unit footage (i.e., the variance between the rates of change of resistance per unit footage of the two drilling rigs) between the two historical drilling rig construction characteristics.

[0032] The second sub-step involves updating the initial characteristic baseline information based on the generated average resistance per unit feed, variance of resistance per unit feed, average rate of change of resistance per unit feed, and variance of rate of change of resistance per unit feed. In practice, the executing entity can use the generated average resistance per unit feed, variance of resistance per unit feed, average rate of change of resistance per unit feed, and variance of rate of change of resistance per unit feed to weight the initial characteristic baseline information, including the initial average resistance per unit feed, initial variance of resistance per unit feed, initial average rate of change of resistance per unit feed, and initial variance of rate of change of resistance per unit feed. For example, the updated initial average resistance per unit feed = (1-α) × initial average resistance per unit feed + α × average resistance per unit feed.

[0033] The third step involves real-time calibration of the updated initial feature baseline information based on the historical drilling rig construction features that meet the temporal proximity condition in the aforementioned historical drilling rig construction feature sequence and the aforementioned real-time drilling rig construction features, thereby obtaining the construction feature baseline information. The temporal proximity condition can be that the acquisition time included in the historical drilling rig construction features and the acquisition time included in the aforementioned real-time drilling rig construction features are only separated by one acquisition cycle. In practice, firstly, the executing entity can generate the average resistance per unit footage, variance per unit footage resistance, average rate of change of footage resistance, and variance of the rate of change of footage resistance between the historical drilling rig construction features that meet the temporal proximity condition and the aforementioned real-time drilling rig construction features. Then, the executing entity can update the initial average resistance per unit footage, initial variance per unit footage resistance, initial average rate of change of footage resistance, and variance of the rate of change of footage resistance included in the initial feature baseline information using the following expression: μ R =(1-α)×μ R +α×μ R(t) μ dR =(1-α)×μ dR +α×μ dR(t) σ R =(1-α)×σ R +α×σ R(t) and σ dR =(1-α)×σ dR +α×σ dR(t) Where α is the weighting weight (for example, it can be 0.25). μ R μ dR σ R and σ dR These represent the initial average resistance per unit advance, the initial resistance per unit advance variance, the initial resistance per unit advance average rate of change, and the initial resistance per unit advance variance, respectively. R(t) μ dR(t) σ R(t) and σ dR(t) These can be the average resistance per unit footage, variance of resistance per unit footage, average rate of change of resistance per unit footage, and variance of the rate of change of resistance per unit footage, respectively, between the historical drilling rig construction characteristics that meet the time-series adjacency condition and the aforementioned real-time drilling rig construction characteristics. t represents the acquisition time included in the aforementioned real-time drilling rig construction characteristics.

[0034] like Figure 2The diagram illustrates the baseline state calibration, where 201 represents the historical drilling rig construction feature sequence, 202 represents the initial feature baseline information, and 203 represents the real-time drilling rig construction features. 204 is a sliding window, its movement direction indicated by the red arrow, sliding downwards. Initially, the initial feature baseline information 202 is generated from the first and second historical drilling rig construction features in the historical drilling rig construction feature sequence 201, marked by the sliding window. Then, the sliding window 204 slides down one step, and the two historical drilling rig construction features defined by the sliding window perform historical state calibration on the initial feature baseline information 202 to update it. This continues until the sliding window defines the real-time drilling rig construction feature 203 and its temporally adjacent historical drilling rig construction features. Both perform real-time state calibration on the updated initial feature baseline information 202 to obtain the construction feature baseline information.

[0035] Step 103: Based on the construction feature benchmark information, perform gas eruption detection on the real-time drilling rig construction features to generate a gas eruption prevention and control strategy, and execute the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy.

[0036] In some embodiments, the aforementioned execution entity can perform gas eruption detection on the aforementioned real-time drilling rig construction characteristics based on the aforementioned construction characteristic benchmark information, in order to generate a gas eruption prevention and control strategy, and execute the gas eruption prevention and control operation corresponding to the aforementioned gas eruption prevention and control strategy. In practice, firstly, the aforementioned execution entity can standardize the drilling rig unit-foot resistance and the rate of change of foot resistance included in the aforementioned real-time drilling rig construction characteristics using the aforementioned construction characteristic benchmark information, which includes the average resistance per unit foot, the variance of the resistance per unit foot, the average rate of change of foot resistance, and the variance of the rate of change of foot resistance. This yields the standardized unit-foot resistance and the standardized rate of change of foot resistance. Then, the obtained standardized unit-foot resistance and the standardized rate of change of foot resistance are weighted and summed to obtain the gas eruption risk value score(t). Afterwards, the aforementioned execution entity can use the following formula to map the probability P of a gas eruption. risk :P risk =1 / (1+exp(score(t))). Finally, the aforementioned execution entity can select at least one blowout prevention instruction corresponding to the aforementioned gas blowout probability from the pre-constructed blowout prevention instruction set as a gas blowout prevention strategy, and execute the corresponding blowout prevention instruction according to the gas blowout prevention strategy. Each blowout prevention instruction in the aforementioned blowout prevention instruction set represents a blowout prevention operation and has a corresponding priority label representing the operation priority. The aforementioned blowout prevention operation may include, but is not limited to: sending a prompt message to the monitoring terminal, limiting drilling rig equipment parameters (e.g., reducing drilling rig thrust or drilling rig spindle speed), activating the blowout preventer, and forcibly shutting down the drilling rig equipment.

[0037] In some optional implementations of certain embodiments, the aforementioned execution entity may perform gas eruption detection on the aforementioned real-time drilling rig construction features based on the aforementioned construction feature reference information through the following steps to generate a gas eruption prevention and control strategy, and execute the gas eruption prevention and control operation corresponding to the aforementioned gas eruption prevention and control strategy: The first step is to standardize the drilling unit footage resistance and the rate of change of drilling resistance, which are included in the above-mentioned real-time drilling rig construction characteristics, based on the aforementioned construction characteristic benchmark information, to obtain the standardized unit footage resistance and the standardized rate of change of drilling resistance. In practice, the aforementioned implementing entity can standardize the drilling unit footage resistance and the rate of change of drilling resistance, which are included in the above-mentioned real-time drilling rig construction characteristics, using the following expression: Standardized unit footage resistance z R(t) =(R(t)-μ R ) / σ R Standard post-feedback resistance change rate z dR(t) =(dR(t)-μ dR ) / σ dR Where R(t) is the drilling unit footage resistance included in the above-mentioned real-time drilling rig construction characteristics. dR(t) is the rate of change of the footage resistance included in the above-mentioned real-time drilling rig construction characteristics.

[0038] The second step is to generate the cumulative deviation of the feature based on the unit advance resistance and the rate of change of the advance resistance after the above standard. In practice, the above-mentioned executing entity can generate the cumulative deviation of the feature S(t) using the following expression: S(t) = S(t-1) + Max(0, (z) R(t) +z dR(t) -k)). Here, S(t-1) can be the cumulative deviation of the feature generated in the acquisition cycle before the current acquisition time t. k can be the deviation error value; values ​​less than or equal to k can be considered normal construction fluctuations and are not included in the cumulative deviation. Max() indicates taking the maximum value in parentheses.

[0039] The third step involves generating gas eruption risk information based on the aforementioned standardized unit advance resistance, the aforementioned standardized advance resistance change rate, the aforementioned cumulative deviation of features, and a pre-trained gas eruption detector. This gas eruption risk information can be a gas eruption probability. In practice, the executing entity can concatenate the aforementioned standardized unit advance resistance, the aforementioned standardized advance resistance change rate, and the aforementioned cumulative deviation of features into a gas eruption detection feature, and input it into the aforementioned gas eruption detector, along with the output gas eruption probability and gas eruption risk information. The aforementioned gas eruption detector can be a machine learning model used to detect and classify gas eruption detection features. The aforementioned gas eruption detector can be a classification model trained on a historical gas eruption detection feature set. The aforementioned historical gas eruption detection feature set can be various gas eruption detection features generated during different sampling periods in the drilling construction phase, and each gas eruption detection feature corresponds to a gas eruption detection label. For example, a gas blowout detection label of 1 indicates that a gas blowout or a clear gas blowout sign occurred within the corresponding acquisition period or within three adjacent acquisition period intervals. A gas blowout detection label of 0 indicates that no gas blowout or a clear gas blowout sign occurred within the corresponding acquisition period or within three adjacent acquisition period intervals. As an example, the aforementioned gas blowout detector can be, but is not limited to, a gradient boosting tree model, a support vector machine model, or a decision tree model. The aforementioned standardized unit penetration resistance, the aforementioned standardized penetration resistance change rate, and the aforementioned cumulative deviation of features can respectively characterize the abnormal amplitude, abnormal change rate, and abnormal duration of gas in the borehole.

[0040] The aforementioned gas nozzle detector can be implemented using a gradient boosting tree model structure. A classification model is constructed by progressively stacking multiple decision trees to map the gas nozzle detection features generated during drilling operations to corresponding gas nozzle risk information. Specifically, the implementing entity can use the standard post-standard unit footage resistance, the standard post-standard footage resistance change rate, and the cumulative feature deviation as gas nozzle detection features, and use these features as model input. The gas nozzle detector comprises multiple sequentially constructed decision trees, each used to correct the deviation of the prediction results from the previous stage. During the model training phase, the implementing entity can construct training samples based on a historical gas nozzle detection feature set. Each training sample includes the historical gas nozzle detection features at the corresponding time point and the corresponding nozzle detection label. The gas nozzle detector can progressively train multiple decision trees with the goal of minimizing the error between the predicted results and the actual nozzle detection labels, allowing subsequent decision trees to focus on learning abnormal features that the preceding decision trees failed to accurately represent. During the model inference phase, the aforementioned execution entity can sequentially input the real-time acquired gas eruption detection features into the multiple decision trees. Each decision tree outputs a corresponding risk prediction value, and these risk prediction values ​​are weighted and summed to generate a comprehensive prediction result. Furthermore, the execution entity can perform probability mapping processing on the comprehensive prediction result to obtain the gas eruption probability, representing the likelihood of a gas eruption occurring under the current construction state, and output this gas eruption probability as gas eruption risk information. By employing the aforementioned gas eruption detector, stable prediction and classification of eruption risks during the drilling construction phase can be achieved by fully utilizing the nonlinear relationships between construction anomaly features, thereby avoiding misjudgments caused by single threshold judgments and improving the accuracy and reliability of gas eruption risk identification.

[0041] The fourth step involves generating a gas eruption prevention and control strategy based on the pre-constructed eruption prevention and control instruction set and the aforementioned gas eruption risk information, and then executing the gas eruption prevention and control operations corresponding to this strategy. In practice, the executing entity can select at least one eruption prevention and control instruction from the instruction set that corresponds to the aforementioned gas eruption risk information, and concatenate them according to their respective operation priorities to obtain the gas eruption prevention and control strategy, and then execute the various gas eruption prevention and control operations corresponding to this strategy.

[0042] In some optional implementations of certain embodiments, the aforementioned execution entity can generate a gas eruption prevention and control strategy based on a pre-built eruption prevention and control instruction set and the aforementioned gas eruption risk information, and execute the gas eruption prevention and control operation corresponding to the aforementioned gas eruption prevention and control strategy through the following steps: First step, determine the gas blowout risk level according to the gas blowout probability included in the above gas blowout risk information. In practice, the above-mentioned execution entity can determine the gas blowout risk level characterized by the above gas blowout risk information (i.e., gas blowout probability) through preset probability thresholds P1, P2, and P3 (P1 < P2 < P3). As an example, when the above gas blowout probability is greater than or equal to P3, determine the gas blowout risk level as a high risk level. When the above gas blowout probability is greater than or equal to P2 and less than P3, determine the gas blowout risk level as a medium risk level. When the above gas blowout probability is greater than or equal to P1 and less than P2, determine the gas blowout risk level as a low risk level. The above gas blowout risk level is used to characterize the degree of danger of gas blowout during the drilling construction process. The low risk level can characterize that during the current drilling construction process, the overall drilling rig construction characteristics are near the reference state, the resistance per unit advance of the drilling rig changes slowly with the construction progress, and the change rate of the advance resistance is small. The drilling process is stable and continuous. No obvious abnormal changes are detected within the reference state range. This is often due to short-term parameter fluctuations caused by changes in coal seam homogeneity or normal equipment adjustment, and the possibility of gas blowout is relatively low. The medium risk level may refer to detecting a certain degree of construction abnormality during the current drilling construction process. For example, the abnormal construction characteristics continue to accumulate within a specific drilling depth range (the change rate of the advance resistance fluctuates greatly, and the resistance per unit advance of the drilling rig is significantly higher than the reference state), or the coal body structure changes, or the gas emission increases. The possibility of gas blowout increases, but it has not reached the level of immediate blowout. Intervention measures such as restricting the drilling rig parameters or starting the blowout prevention device in advance are required to prevent further evolution into a high risk state and the occurrence of a blowout event. The high risk level can characterize detecting significant construction abnormalities during the current drilling construction process. For example, the coal body around the borehole is strongly disturbed, the gas release rate increases significantly, the resistance per unit advance of the drilling rig is significantly higher than the reference state, or the change rate of the advance resistance changes rapidly and significantly. The possibility of gas blowout is relatively high, and there is a high probability of a blowout accident.

[0043] Second step, select at least one blowout prevention and control instruction corresponding to the above gas blowout risk level from the above blowout prevention and control instruction set as the gas blowout prevention and control strategy. In practice, each gas blowout risk level corresponds to at least one blowout prevention and control instruction, representing a set of gas blowout prevention and control strategies under the corresponding blowout risk.

[0044] The third step, in response to the determination that the gas eruption risk level is high, involves sequentially executing the drilling rig shutdown, blowout preventer activation, and monitoring terminal alert operation according to the operational sequence indicated by the gas eruption prevention strategy. The drilling rig shutdown operation refers to immediately stopping the drilling rig's rotation and propulsion, preventing further damage to the coal seam structure by the drill bit, achieved by sending a shutdown command to the drilling rig control system. The blowout preventer activation operation can refer to activating a blowout preventer device located near the borehole opening to block or mitigate the emission of gas and coal dust. This blowout preventer device can be a blowout preventer valve or a blowout preventer cover. The monitoring terminal alert operation involves sending risk warning information to the monitoring terminal to indicate the current gas eruption risk level or probability.

[0045] The fourth step, in response to determining the risk level in the aforementioned gas eruption risk level characterization, involves sequentially executing the blowout prevention device activation operation, drilling rig parameter limitation operation, and monitoring terminal prompt operation, according to the operational sequence characterized by the aforementioned gas eruption prevention strategy. The aforementioned drilling rig parameter limitation operation may refer to limiting or reducing certain operating parameters of the drilling rig (such as drilling rig thrust or drilling rig spindle speed) to slow down the coal seam disruption rate or reduce the gas release intensity.

[0046] The fifth step is to respond to the determination that the risk level of the gas eruption is low and to execute the monitoring terminal prompt operation indicated by the gas eruption prevention and control strategy.

[0047] Step 104: Perform gas anomaly monitoring on the constructed gas feature sequence to generate gas anomaly classification information for each gas.

[0048] In some embodiments, the aforementioned executing entity can perform gas anomaly monitoring on the constructed gas characteristic sequence to generate various gas anomaly classification information. The aforementioned gas characteristic sequence is generated based on gas parameter sequences obtained from periodically extracted gas from completed coal mine boreholes and connected goafs or sealed areas. The aforementioned gas parameter sequence can be composed of various gas parameters obtained from gas collection at different times within the same sampling period from a single completed coal mine borehole and connected goaf or sealed area. Furthermore, during each gas extraction in different sampling periods, the extraction order of the coal mine borehole, goaf, and sealed area is the same, thereby ensuring that the gas characteristic sequence and the gas characteristics at the same position in the historical gas characteristic sequence represent the gas characteristics within the same area. Each gas parameter in the gas parameter sequence includes: methane concentration, carbon monoxide concentration, extraction flow rate, collection negative pressure, collection time, and collection temperature. Each gas parameter corresponds to a collection area label to characterize the data source. The methane concentration mentioned above refers to the volume fraction of methane gas in the extracted gas from the corresponding borehole, goaf, or confined area, usually expressed as a percentage (%), and can be measured by a methane sensor or gas sensor. The carbon monoxide concentration mentioned above refers to the volume fraction of carbon monoxide gas in the extracted gas, usually expressed in ppm or mg / m³. 3 This indicates that it can be measured by a carbon monoxide sensor. The aforementioned extraction flow rate can refer to the volume or mass of methane gas extracted through the borehole extraction pipeline per unit time, typically expressed in cubic meters per second (m³). 3 / min or m 3 / h represents the flow rate. The flow rate is measured using a flow meter. The aforementioned negative pressure refers to the negative pressure value formed within the extraction pipeline relative to the external environment during the drilling and extraction process, usually expressed in Pa or kPa, and measured by a negative pressure sensor. The aforementioned temperature refers to the temperature value of the gas in the borehole or pipeline during the extraction process, usually expressed in degrees Celsius (°C). In practice, the aforementioned executing entity can concatenate the various parameters included in the gas parameter sequence as gas features to obtain a gas feature sequence. Then, the aforementioned executing entity can directly monitor gas anomalies in the constructed gas feature sequence using a gas anomaly classification model to generate various gas anomaly classification information. Each gas anomaly classification information characterizes the cause of the gas anomaly in the corresponding collection area.

[0049] In some optional implementations of certain embodiments, the aforementioned execution entity may perform gas anomaly monitoring on the constructed gas feature sequence through the following steps to generate various gas anomaly classification information: The first step is to standardize the aforementioned gas characteristic sequence based on historical gas characteristic sequences to update the aforementioned borehole gas characteristic sequence. In practice, for each gas characteristic in the aforementioned gas characteristic sequence, the executing entity first identifies the historical gas characteristic with the same position number as the aforementioned gas characteristic in the historical gas characteristic sequence as the target historical gas characteristic. Then, the executing entity generates the mean and variance of each characteristic value in the aforementioned borehole gas characteristic and the target historical gas characteristic, and standardizes the characteristic values ​​using the determined mean and variance to standardize the aforementioned borehole gas characteristic.

[0050] The second step involves performing gas anomaly detection on the updated gas feature sequence based on a pre-built gas anomaly detector to generate individual gas anomaly detection information. This gas anomaly detector can be a machine learning model that takes gas features as input and outputs gas anomaly detection information. For example, it could be an isolated forest model or a single-class support vector machine model. The gas anomaly detection information can be gas anomaly detection labels. For instance, a gas anomaly detection label of 1 indicates that the corresponding gas feature is an anomalous gas feature. In practice, the executing entity can input the updated gas feature sequence one by one into the gas anomaly detector to obtain the corresponding gas anomaly detection labels as gas anomaly detection information.

[0051] The third step is to determine the information of each gas anomaly event based on the aforementioned gas anomaly detection information. In practice, firstly, the executing entity can group together gas gas features that are characterized as abnormal gas features, have adjacent corresponding acquisition times, and share the same acquisition area label. Then, for each gas gas feature group, the executing entity can use the mean value of each gas gas feature within the group as the gas gas baseline feature, concatenate the maximum values ​​of each gas gas feature under each dimension to obtain the gas gas anomaly feature, and determine the acquisition time interval corresponding to each gas gas feature as the start and end time of the gas anomaly. Finally, the executing entity can determine the gas gas anomaly feature, the gas gas baseline feature, and the gas anomaly start and end time as the gas anomaly event information.

[0052] Fourth, for each of the above gas anomaly events, perform the following monitoring and processing steps: The first sub-step involves generating a gas characteristic change quantity based on the gas baseline characteristics and gas anomaly characteristics included in the aforementioned gas anomaly event information. In practice, the executing entity can subtract the aforementioned gas anomaly characteristics from the aforementioned gas baseline characteristics dimension by dimension to generate the gas characteristic change quantity.

[0053] The second sub-step involves generating gas anomaly classification information based on the aforementioned gas characteristic changes and a pre-built gas anomaly classification model. This gas anomaly classification information characterizes the cause of gas anomalies in the corresponding area. In practice, the executing entity can input the aforementioned gas characteristic changes into the pre-built gas anomaly classification model to generate gas anomaly classification information. This gas anomaly classification model can be a multi-classification model that takes gas characteristic changes as input and at least one gas anomaly classification label as output as gas anomaly classification information. As an example, the aforementioned GBDT (Gradient Boosting Decision Trees) model. The aforementioned gas anomaly classification labels can include C1, C2, C3, C4, and C5. C1 characterizes the abnormal gas state caused by blockage in the borehole or its extraction channel, characterized by a significant decrease in extraction flow rate while the extraction negative pressure is maintained or increased. C2 characterizes the presence of a gas leak or air leak in the extraction system, causing outside air to mix with the extracted gas, thus triggering abnormal gas parameters, characterized by a decrease in extraction flow rate and extraction negative pressure, accompanied by fluctuations or decreases in methane concentration. C3 represents gas parameter changes caused by abnormal operation of the extraction system equipment, rather than abnormalities in the coal body or borehole itself. Its defining characteristic is a significant decrease in extraction negative pressure, accompanied by a decrease in extraction flow rate. C4 represents gas parameter abnormalities caused by increased gas release intensity within the coal body. This is the type of abnormality most directly related to gas risk. Its defining characteristic is an increase in methane concentration, not necessarily accompanied by a simultaneous decrease in extraction negative pressure or extraction flow rate. C5 represents monitoring data abnormalities caused by abnormalities in the gas parameter acquisition equipment itself, rather than changes in actual gas conditions. Its defining characteristic is a sudden change in methane or carbon monoxide concentration, but the extraction flow rate and extraction negative pressure remain constant or fluctuate.

[0054] like Figure 3 As shown, after performing gas anomaly detection on the gas feature sequence 301, a gas anomaly event information set 302 (i.e., information on each gas anomaly event) can be obtained. Through the gas anomaly classification model 303, gas anomaly classification information corresponding to each gas anomaly event information can be generated, resulting in gas anomaly classification information 304.

[0055] The relevant content in steps one through four above constitutes an inventive point of this disclosure, solving the technical problem that "during the extraction stage after drilling is completed, gas parameters typically exhibit multi-dimensional variation characteristics. Different anomalies may manifest as similar fluctuations in a single parameter, making it difficult to accurately distinguish between actual gas anomalies and equipment fluctuations or acquisition errors based solely on a single threshold or single parameter change. Furthermore, gas anomalies often exhibit continuity and regional correlation; judging solely based on instantaneous detection results can easily lead to false alarms or missed alarms, thereby reducing the safety of gas monitoring." Solving these factors can improve the safety of gas monitoring. To achieve this, firstly, the gas characteristic sequence is standardized to eliminate the influence of data distribution differences between different boreholes or time periods. Then, anomaly detection models are used to identify abnormal gas characteristics, and anomaly events are aggregated based on temporal adjacency and regional consistency to avoid misjudgments caused by isolated anomalies. Based on this, by constructing gas characteristic changes and inputting them into the gas anomaly classification model, a fine distinction can be made between different anomaly causes. This enables the clear identification of situations such as blockage, gas leakage, equipment malfunction, enhanced gas release, and sensor malfunction, thereby improving the accuracy and safety of gas anomaly monitoring.

[0056] Step 105: Based on the gas nozzle control strategy and the gas anomaly classification information, generate a gas monitoring and control strategy, and send the gas monitoring and control strategy to the monitoring terminal.

[0057] In some embodiments, the executing entity can generate a gas monitoring and control strategy based on the gas nozzle control strategy and the various gas anomaly classification information, and send the gas monitoring and control strategy to the monitoring terminal. The monitoring terminal can be a terminal device for monitoring coal mine operations. In practice, the executing entity can use the collection area label corresponding to each gas anomaly classification information and the gas nozzle control strategy as the gas monitoring and control strategy, and send it to the monitoring terminal.

[0058] In some optional implementations of certain embodiments, the aforementioned execution entity can generate a gas monitoring and control strategy based on the aforementioned gas nozzle control strategy and the aforementioned gas anomaly classification information through the following steps: The first step is to generate gas risk status information based on the aforementioned gas eruption control strategy and the various gas anomaly classification information. In practice, the implementing entity can perform a scoring mapping between the gas eruption risk level corresponding to the gas eruption control strategy generated during the drilling construction phase and the various gas anomaly classification information generated during the extraction phase. Specifically, the implementing entity can generate corresponding construction phase risk values ​​according to the eruption risk level corresponding to the aforementioned gas eruption control strategy. The eruption risk level can be mapped to preset construction phase risk values; for example, low-risk, medium-risk, and high-risk levels can be mapped to first, second, and third construction risk values, respectively. Then, the implementing entity can sum the gas anomaly classification labels (i.e., each label corresponds to a fixed risk value) included in the various gas anomaly classification information to generate corresponding extraction phase risk values. Afterward, the implementing entity can perform a weighted summation of the mapped construction risk values ​​and the risk values ​​of each extraction phase to obtain gas risk status information characterizing the gas risk level. The aforementioned gas risk status information is used to characterize the overall gas safety status of coal mine boreholes under the combined effects of the construction and extraction phases. A low-risk status indicates that no significant gas-related anomalies have been detected in the current borehole during either the construction or extraction phases, and the overall gas safety status is good. A low-risk status may include the following: no significant blowout risk detected during the construction phase; gas parameters during the extraction phase are generally within the normal range of variation or only exhibit slight, short-term, and self-recoverable fluctuations. A medium-risk status indicates that a certain degree of gas-related anomalies have been detected in the current borehole during at least one phase of the construction or extraction phase, but have not yet reached an out-of-control state. A medium-risk status may include the following: a medium-level blowout risk detected during the construction phase or gas anomaly classification information detected during the extraction phase; methane concentration or extraction flow rate shows a continuous deviation but at a low rate of change. A high-risk status indicates that significant gas-related anomalies have been detected in the current borehole during the construction and / or extraction phases, the gas risk has reached an urgent level, and there is a risk of a gas accident. High-risk conditions can include the following: high-level gas venting risk detected during the drilling phase; severe gas anomalies during the extraction phase, with the anomaly type indicating rapid gas release; and drastic changes in methane concentration or extraction flow rate within a short period of time.

[0059] The second step involves selecting at least one gas monitoring and control instruction from a pre-set set of gas monitoring and control instructions that corresponds to the aforementioned unified gas risk status information, as the gas monitoring and control strategy. Each gas monitoring and control instruction in the aforementioned set represents a gas monitoring and control operation. These operations may include, but are not limited to, adjustments to extraction parameters, triggering of abnormal situations, and alerts or alarms from monitoring terminals.

[0060] The third step, in response to the high-risk status indicated by the aforementioned gas risk status information, involves executing enhanced gas extraction control, triggering abnormal handling operations, and issuing alarms or alerts at the monitoring terminal, following the operational sequence described in the aforementioned gas monitoring and control strategy. Enhanced gas extraction control may refer to increasing the extraction negative pressure, increasing the extraction flow rate, or increasing the operating intensity of the extraction equipment during gas extraction. Triggering abnormal handling operations may involve initiating a specialized handling procedure for abnormal gas conditions (e.g., initiating an inspection procedure for blockages, leaks, or equipment malfunctions, or triggering on-site manual confirmation). The monitoring terminal alarm or alert operation may refer to issuing risk alarms or alerts at the monitoring terminal (e.g., issuing risk alarms for high-risk conditions and risk alerts for medium- and low-risk conditions).

[0061] The fourth step is to respond to the risk status in the above-mentioned gas risk status information and, in accordance with the operation sequence represented by the above-mentioned gas monitoring and control strategy, to perform enhanced gas extraction control and monitoring terminal alarm or prompt operations.

[0062] Fifth, in response to the low-risk status indicated by the above gas risk status information, execute the monitoring terminal prompts corresponding to the above gas monitoring and control strategy.

[0063] The aforementioned steps one through five constitute an inventive point of this disclosure, addressing the technical problem that "gas monitoring typically involves independent assessments of either the drilling or extraction stages, leading to fragmented risk information, inconsistent expression, and difficulty in reflecting the continuous evolution of gas release across different operational stages. When the risk of perforations during the drilling stage and gas anomalies during the extraction stage coexist or interact, the lack of a unified risk quantification and monitoring mechanism reduces the overall stability of gas monitoring and the safety of drilling operations." Solving these factors can improve the safety of gas monitoring during drilling operations. To achieve this, risk value mapping and weighted fusion are performed on the perforation risk results during the drilling stage and the gas anomaly information during the extraction stage to generate gas risk status information that uniformly characterizes the gas risk status across all stages. Based on this unified risk status, a tiered gas monitoring and control strategy is implemented, enabling unified quantification and collaborative management of risk information across different operational stages. This improves the completeness and continuity of gas risk assessment, enhances the matching between control measures and actual risk levels, and further improves the overall safety and monitoring stability of coal mine drilling operations.

[0064] The above-described embodiments of this disclosure have the following beneficial effects: the full-process gas monitoring and control method for coal mine drilling, as described in some embodiments of this disclosure, can perform full-process gas monitoring and control of coal mine drilling operations, thereby improving the stability of gas anomaly monitoring and thus enhancing drilling operation safety. Specifically, the reason for poor gas monitoring stability and reduced safety in coal mine drilling operations is that when a single-stage gas monitoring method is used, the monitoring parameters only reflect the instantaneous state of the current stage, making it difficult to reflect the continuous changes in gas release and gas evolution between different operation stages. Furthermore, within a single stage, gas parameters are easily affected by construction conditions, extraction conditions, or environmental factors. Relying solely on local parameter changes makes it difficult to accurately distinguish between normal fluctuations and abnormal gas changes, resulting in poor gas monitoring stability and thus reducing the safety of coal mine drilling operations. Based on this, the full-process gas monitoring and control method for coal mine drilling, as described in some embodiments of this disclosure, firstly constructs real-time drilling rig construction characteristics based on the real-time collected drilling rig operation parameters. These drilling rig operation parameters are collected from the drilling equipment currently performing drilling operations in the coal mine. Therefore, by collecting drilling rig operating parameters in real time and constructing construction features, the operating status of the drilling equipment during drilling can be reflected, thereby quantifying the disturbance of the coal seam and the potential gas release status during the drilling stage. Then, based on the historical drilling rig construction feature sequence, the aforementioned real-time drilling rig construction features are calibrated to a baseline state to generate construction feature baseline information. This baseline information represents the baseline construction state of the drilling equipment within the coal mine borehole at the current construction progress. Thus, by calibrating the real-time construction features against historical construction features, the construction features of the same borehole at different construction progresses are comparable, reducing interference from normal fluctuations caused by differences in coal seam conditions, abnormally slow gas rise, or differences in borehole depth, and improving the stability of identifying abnormal construction states. Subsequently, based on the aforementioned construction feature baseline information, gas eruption detection is performed on the aforementioned real-time drilling rig construction features to generate a gas eruption prevention and control strategy, and to execute the corresponding gas eruption prevention and control operations. Therefore, by detecting blowouts based on the deviation of construction characteristics from the baseline state, the assessment of blowout risk can be made independent of absolute thresholds, but rather correlated with the actual construction status of the current borehole. This allows for more timely and stable identification of blowout risks caused by gas release triggered by borehole construction, and enables the implementation of corresponding prevention and control measures in advance. Secondly, gas anomaly monitoring is performed on the constructed gas characteristic sequence to generate various gas anomaly classification information. The aforementioned gas characteristic sequence is generated based on the gas parameter sequence of periodically extracted from completed coal mine boreholes and connected goafs or confined areas.Therefore, by continuously monitoring gas parameters during the extraction phase after drilling, abnormal changes in parameters such as gas concentration and extraction flow rate can be identified holistically, rather than relying on the instantaneous concentration of a single parameter at a single moment. This improves the accuracy and stability of gas anomaly detection. Finally, based on the gas eruption control strategy and the gas anomaly classification information, a gas monitoring and control strategy is generated and sent to the monitoring terminal. Thus, by integrating the eruption control strategy during the drilling construction phase with the gas anomaly classification information during the extraction phase, the gas control strategy can simultaneously reflect the eruption risk during the construction phase and the continuous changes in gas during the extraction phase. This reduces monitoring errors or omissions caused by monitoring in a single phase, improves the stability and continuity of gas anomaly identification, and ultimately enhances the overall safety of coal mine drilling operations.

[0065] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a full-process gas monitoring and control device for coal mine boreholes. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this full-process gas monitoring and control device for coal mine boreholes can be specifically applied to various electronic devices.

[0066] like Figure 4As shown, a full-process gas monitoring and control device 400 for coal mine boreholes in some embodiments includes: a construction unit 401, a reference state calibration unit 402, a gas eruption detection unit 403, a gas anomaly monitoring unit 404, and a generation unit 405. The construction unit 401 is configured to construct real-time drilling rig construction features based on real-time collected drilling rig operation parameters, wherein the drilling rig operation parameters are collected from the drilling equipment currently drilling in the coal mine. The reference state calibration unit 402 is configured to perform reference state calibration on the real-time drilling rig construction features based on historical drilling rig construction feature sequences to generate construction feature reference information, wherein the construction feature reference information represents the reference construction state of the drilling equipment in the coal mine borehole at the current construction progress. The gas eruption detection unit 403 is configured to detect gas eruptions on the real-time drilling rig construction features based on the construction feature reference information. The system generates a gas eruption prevention and control strategy and executes the gas eruption prevention and control operations corresponding to the aforementioned gas eruption prevention and control strategy. The gas anomaly monitoring unit 404 is configured to monitor gas anomalies in the constructed gas characteristic sequence to generate various gas anomaly classification information. The aforementioned gas characteristic sequence is generated based on the gas parameter sequence of periodically extracted from coal mine boreholes that have completed drilling and the connected goaf or closed area. The generation unit 405 is configured to generate a gas monitoring and control strategy based on the aforementioned gas eruption prevention and control strategy and the aforementioned various gas anomaly classification information, and send the aforementioned gas monitoring and control strategy to the monitoring terminal.

[0067] It is understandable that the various units recorded in the 400 full-process gas monitoring and control device for coal mine boreholes are similar to those in the reference system. Figure 1 The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the full-process gas monitoring and control device 400 for coal mine boreholes and the units contained therein, and will not be repeated here.

[0068] The following is for reference. Figure 5 It shows a schematic diagram of the structure of an electronic device 500 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0069] like Figure 5As shown, the electronic device 500 may include a processing unit 501 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0070] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0071] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, it performs the functions defined in the methods of some embodiments of this disclosure.

[0072] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0073] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0074] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: construct real-time drilling rig construction features based on real-time collected drilling rig operation parameters, wherein the drilling rig operation parameters are collected from the drilling equipment currently drilling in the coal mine; calibrate the aforementioned real-time drilling rig construction features to a reference state based on historical drilling rig construction feature sequences to generate construction feature reference information, wherein the construction feature reference information characterizes the reference construction state of the aforementioned drilling equipment within the coal mine borehole at the current construction progress; and calibrate the aforementioned real-time drilling rig construction features to a reference state based on the aforementioned construction feature reference information. The system detects gas eruptions based on the drilling rig's construction characteristics to generate a gas eruption prevention and control strategy, and executes the corresponding gas eruption prevention and control operations. It also monitors gas anomalies in the constructed gas characteristic sequence to generate various gas anomaly classification information. This gas characteristic sequence is generated based on gas parameter sequences periodically extracted from completed coal mine boreholes and connected goaf or sealed areas. Based on the gas eruption prevention and control strategy and the various gas anomaly classification information, a gas monitoring and control strategy is generated and sent to the monitoring terminal.

[0075] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0077] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0078] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for full-process gas monitoring and control in coal mine boreholes, characterized in that, include: Based on the real-time collected drilling rig operation parameters, a real-time drilling rig construction feature is constructed, wherein the drilling rig operation parameters are collected from the drilling equipment that is drilling in the coal mine. Based on the historical drilling rig construction characteristic sequence, the real-time drilling rig construction characteristics are calibrated to a baseline state to generate construction characteristic baseline information, wherein the construction characteristic baseline information represents the baseline construction state of the drilling equipment in the coal mine borehole under the current construction progress. Based on the construction feature reference information, gas eruption detection is performed on the real-time drilling rig construction features to generate a gas eruption prevention and control strategy, and to execute the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy. Gas anomaly monitoring is performed on the constructed gas feature sequence to generate various gas anomaly classification information. The gas feature sequence is generated based on the gas parameter sequence of periodically extracted from coal mine boreholes that have completed drilling and the connected goaf or closed area. Based on the gas nozzle control strategy and the gas anomaly classification information, a gas monitoring and control strategy is generated and sent to the monitoring terminal.

2. The method according to claim 1, characterized in that, The process of constructing real-time drilling rig construction characteristics based on real-time collected drilling rig operation parameters includes: The drilling speed is generated based on the drilling depth included in the drilling rig operating parameters and the drilling depth included in the historical drilling rig operating parameters. Based on the drilling rig operating parameters, including drilling rig torque and drilling rig thrust, the drilling rig unit footage resistance is generated; Based on the generated drilling rig unit footage resistance, generate the footage resistance change rate; The drilling rig operating parameters, including acquisition time, drilling depth, drilling rig rotation speed, drilling rig advance speed, drilling rig unit advance resistance, and advance resistance change rate, are spliced ​​together to form real-time drilling rig construction characteristics.

3. The method according to claim 1, characterized in that, The step of calibrating the real-time drilling rig construction characteristics based on historical drilling rig construction characteristic sequences to generate construction characteristic benchmark information includes: Initial feature reference information is generated based on the first historical drilling rig construction features and the second historical drilling rig construction features in the historical drilling rig construction feature sequence. Based on the historical drilling rig construction features that meet the positional conditions in the historical drilling rig construction feature sequence, the initial feature reference information is calibrated according to its historical state in order to update the initial feature reference information. Based on the historical drilling rig construction features that meet the temporal adjacency condition in the historical drilling rig construction feature sequence and the real-time drilling rig construction features, the updated initial feature reference information is calibrated in real time to obtain the construction feature reference information.

4. The method according to claim 3, characterized in that, The step of calibrating the initial feature reference information based on the historical drilling rig construction features that meet the positional conditions in the historical drilling rig construction feature sequence to update the initial feature reference information includes: For every two historical drilling rig construction features in the historical drilling rig construction feature sequence that meet the positional condition, the following state calibration steps are performed based on the initial feature reference information: Based on the construction characteristics of the two historical drilling rigs, the average resistance per unit footage, the variance of resistance per unit footage, the average rate of change of resistance per footage, and the variance of the rate of change of resistance per footage are generated. The initial characteristic reference information is updated based on the generated average resistance per unit advance, variance of resistance per unit advance, average rate of change of resistance per unit advance, and variance of rate of change of resistance per unit advance.

5. The method according to claim 1, characterized in that, The step of detecting gas eruptions in the real-time drilling rig construction features based on the construction feature reference information to generate a gas eruption prevention and control strategy, and executing the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy, includes: Based on the construction characteristic benchmark information, the drilling rig unit advance resistance and advance resistance change rate, which are included in the real-time drilling rig construction characteristics, are standardized to obtain the standardized unit advance resistance and standardized advance resistance change rate. Based on the standard unit advance resistance and the standard advance resistance change rate, a characteristic cumulative deviation is generated; Based on the standard post-standard unit advance resistance, the standard post-standard advance resistance change rate, the feature cumulative deviation, and the pre-trained gas nozzle detector, gas nozzle risk information is generated. Based on the pre-built set of gas eruption prevention and control instructions and the gas eruption risk information, a gas eruption prevention and control strategy is generated, and the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy is executed.

6. The method according to claim 5, characterized in that, The step of generating a gas eruption prevention and control strategy based on a pre-built eruption prevention and control instruction set and the gas eruption risk information, and executing the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy, includes: Based on the gas eruption risk information, the gas eruption risk level is determined; Select at least one gas nozzle control instruction corresponding to the gas nozzle risk level from the set of nozzle control instructions as the gas nozzle control strategy. In response to determining that the gas eruption risk level is characterized as a high-risk level, the drilling equipment shutdown operation, the blowout prevention device activation operation, and the monitoring terminal prompt operation are executed sequentially according to the operation sequence characterized by the gas eruption prevention strategy. In response to determining the risk level in the gas eruption risk level characterization, the blowout prevention device activation operation, drilling rig parameter limitation operation, and monitoring terminal prompt operation are executed according to the operation sequence characterized by the gas eruption prevention strategy. In response to determining that the risk level of the gas eruption is low, the monitoring terminal prompt operation represented by the gas eruption prevention and control strategy is executed.

7. A full-process gas monitoring and control device for coal mine boreholes, characterized in that, include: The construction unit is configured to construct real-time drilling rig construction features based on real-time collected drilling rig operation parameters, wherein the drilling rig operation parameters are collected from the drilling equipment that is drilling holes in the coal mine. A baseline state calibration unit is configured to perform baseline state calibration on the real-time drilling rig construction features based on a historical drilling rig construction feature sequence to generate construction feature baseline information, wherein the construction feature baseline information characterizes the baseline construction state of the drilling rig equipment in the coal mine borehole under the current construction progress. The gas eruption detection unit is configured to perform gas eruption detection on the real-time drilling rig construction features based on the construction feature reference information, so as to generate a gas eruption prevention and control strategy and execute the gas eruption prevention and control operation corresponding to the gas eruption prevention and control strategy. A gas anomaly monitoring unit is configured to monitor gas anomalies in the constructed gas feature sequence to generate various gas anomaly classification information. The gas feature sequence is generated based on the gas parameter sequence of periodically extracted from coal mine boreholes that have completed drilling and the connected goaf or closed area. The generation unit is configured to generate a gas monitoring and control strategy based on the gas nozzle control strategy and the gas anomaly classification information, and to send the gas monitoring and control strategy to the monitoring terminal.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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