Method and system for evaluating goaf gas concentration distribution under extraction condition and medium

Abstract

The invention provides a goaf gas concentration distribution evaluation method and system under the extraction condition and a medium, and relates to the technical field of gas extraction, and the goaf gas concentration distribution evaluation method comprises the steps that multiple pieces of gas concentration sensing information are collected for gas distribution analysis, and first gas concentration distribution information is obtained; when the first gas concentration distribution information meets the extraction activation condition, gas extraction pressure is configured for the multiple roadway openings; carrying out extraction prediction on the gas concentration sensing information and the gas extraction pressure to obtain multiple pieces of gas concentration prediction information and carrying out gas distribution analysis to obtain second gas concentration distribution information; and when the second gas concentration distribution information does not meet the extraction activation condition, the gas extraction pressure and the second gas concentration distribution information are sent to the client. The technical problem that in the prior art, due to the fact that it is difficult to accurately predict the gas distribution state, the roadway extraction control granularity is large, and the gas extraction efficiency is affected is solved, and the gas extraction control precision, the extraction efficiency and the extraction safety are improved.

Description

Technical Field

[0001] The present application relates to the field of gas extraction technology, and in particular to a method, system and medium for evaluating gas concentration distribution in goaf areas under extraction conditions. Background Art

[0002] Gas extraction is a key link in ensuring safe production in coal mines. During coal mining, gas will gush out from the goaf. If it is not effectively extracted, it is very easy to cause safety accidents. The existing gas extraction methods are mainly divided into closed extraction method, drilling extraction method and tunnel extraction method. The closed extraction method is suitable for occasions where the goaf is relatively closed; the drilling extraction method is suitable for situations where the coal seam has good permeability and uniform gas outflow; the tunnel extraction method is the most widely used due to its strong flexibility and wide adaptability, especially when the tunnel layout is complex and the gas outflow is dispersed.

[0003] In the actual mining process, the ventilation conditions between different tunnels, the gas outburst speed and the changes in coal rock structure will affect the temporal and spatial distribution of gas concentration, and the existing tunnel extraction methods usually lack sufficient monitoring means and cannot fully perceive these factors. The lack of this gas distribution information leads to the blindness of the extraction strategy. Secondly, the dispersion of gas outburst volume further limits the accuracy of extraction control. Due to the significant differences in gas outburst conditions in each tunnel, the existing methods mostly use a unified extraction pressure or flow setting. This control method may not only lead to insufficient gas extraction, but also cause energy waste.

[0004] In summary, the tunnel extraction method is difficult to effectively cope with the dynamic changes of gas distribution under complex coal mine conditions. The extraction control granularity is coarse and the degree of refinement is insufficient, which affects the gas extraction efficiency and also poses a potential threat to the safe production of coal mines. Summary of the invention

[0005] The present application provides a method, system and medium for evaluating the gas concentration distribution in goaf under extraction conditions, which solves the technical problem in the prior art that it is difficult to accurately predict the gas distribution state due to the complex tunnel layout and dispersed gas outburst volume, resulting in coarse granularity of tunnel extraction control and affecting the gas extraction efficiency. The application achieves the technical effect of realizing refined control of gas extraction and thereby improving extraction efficiency and extraction safety.

[0006] In view of the above problems, on the one hand, the present application provides a method for evaluating the gas concentration distribution in a goaf under extraction conditions, which is applied to a system for evaluating the gas concentration distribution in a goaf under extraction conditions, wherein the system is communicatively connected to a plurality of gas concentration sensors, and the plurality of gas concentration sensors are deployed at a plurality of gas extraction tunnel entrances, and the plurality of gas extraction tunnel entrances are connected to the goaf, and the method comprises: communicating with a plurality of gas concentration sensors and receiving a plurality of gas concentration sensor information; performing a gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information; when the first gas concentration distribution information meets the extraction activation condition, configuring a plurality of gas extraction pressures for the plurality of gas extraction tunnel entrances; performing a preset time extraction prediction on the plurality of gas concentration sensor information and the plurality of gas extraction pressures to obtain a plurality of gas concentration prediction information; performing a gas distribution analysis on the plurality of gas concentration prediction information to obtain a second gas concentration distribution information; and when the second gas concentration distribution information does not meet the extraction activation condition, sending the plurality of gas extraction pressures and the second gas concentration distribution information to the client.

[0007] On the other hand, the present application also provides a gas concentration distribution evaluation system for goaf under extraction conditions, the system comprising: a gas concentration sensing module, used to communicate with multiple gas concentration sensors and receive multiple gas concentration sensing information; a first distribution analysis module, used to perform gas distribution analysis on the multiple gas concentration sensing information to obtain first gas concentration distribution information; an extraction pressure configuration module, used to configure multiple gas extraction pressures for multiple gas extraction tunnel entrances when the first gas concentration distribution information meets the extraction activation conditions; an extraction prediction module, used to perform preset duration extraction predictions on the multiple gas concentration sensing information and the multiple gas extraction pressures to obtain multiple gas concentration prediction information; a second distribution analysis module, used to perform gas distribution analysis on the multiple gas concentration prediction information to obtain second gas concentration distribution information; a concentration feedback module, used to send the multiple gas extraction pressures and the second gas concentration distribution information to the client when the second gas concentration distribution information does not meet the extraction activation conditions.

[0008] In a third aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for evaluating gas concentration distribution in goaf under the above-mentioned extraction conditions.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By communicating with multiple gas concentration sensors, the original data of the gas concentration in the goaf is obtained, and multiple gas concentration sensor information is received to provide data support for subsequent analysis and decision-making. By performing gas distribution analysis on the multiple gas concentration sensor information, the current distribution of gas concentration in the goaf, that is, the first gas concentration distribution information, is obtained, which helps to understand the overall state of gas in the goaf and determine whether further extraction measures are needed. When the first gas concentration distribution information meets the extraction activation conditions, multiple gas extraction pressures are configured for multiple gas extraction tunnel entrances, and effective extraction measures are initiated according to the current gas distribution. The multiple gas concentration sensor information and the multiple gas extraction pressures are predicted for a preset time, and multiple gas concentration prediction information is obtained to estimate the extraction effect and make the extraction work more forward-looking. The multiple gas concentration prediction information is analyzed for gas distribution to obtain the second gas concentration distribution information, providing a more accurate basis for the final decision. When the second gas concentration distribution information does not meet the extraction activation condition, the multiple gas extraction pressures and the second gas concentration distribution information are sent to the client, so that relevant personnel can understand the extraction situation in time for subsequent management and decision-making.

[0011] In summary, this application realizes the refined management of gas extraction in goaf areas by deploying multi-point gas concentration sensors, combined with gas distribution analysis, dynamic extraction pressure adjustment and preset duration prediction. This solution can not only monitor the gas concentration distribution in real time, but also adjust the extraction strategy in advance through prediction, which significantly improves the efficiency and safety of gas extraction, reduces the risk of gas accumulation, and provides reliable technical guarantee for safe production in coal mines.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of a method for evaluating gas concentration distribution in goaf under extraction conditions provided in an embodiment of the present application.

[0014] Figure 2 A schematic diagram of the structure of a gas concentration distribution assessment system for goaf areas under extraction conditions provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: gas concentration sensing module 10 , first distribution analysis module 20 , extraction pressure configuration module 30 , extraction prediction module 40 , second distribution analysis module 50 , concentration feedback module 60 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a method, system and medium for evaluating the gas concentration distribution in goaf under extraction conditions, thereby solving the technical problem in the prior art that it is difficult to accurately predict the gas distribution state due to the complex tunnel layout and dispersed gas outburst volume, resulting in coarse granularity of tunnel extraction control and affecting the gas extraction efficiency. The technical effect of realizing refined control of gas extraction and thereby improving extraction efficiency and extraction safety is achieved.

[0017] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for evaluating gas concentration distribution in a goaf under drainage conditions, the method is applied to a system for evaluating gas concentration distribution in a goaf under drainage conditions, the system is communicatively connected to a plurality of gas concentration sensors, the plurality of gas concentration sensors are deployed at a plurality of gas drainage tunnel openings, the plurality of gas drainage tunnel openings are connected to the goaf, and includes:

[0018] Step S1: Communicate with multiple gas concentration sensors and receive multiple gas concentration sensing information.

[0019] Specifically, a gas concentration sensor is a device specifically used to detect the concentration of gas (mainly methane) in the environment, such as an infrared methane sensor or an electrochemical gas sensor. A connection is established with multiple gas concentration sensors through a communication line (such as a cable, a wireless communication module, etc.). The gas concentration sensor transmits the detected gas concentration information in the form of corresponding signals, and then the system receives these signals and converts them into processable gas concentration sensing information.

[0020] By communicating with multiple gas concentration sensors, the collection of gas concentration data is realized, providing the most basic data source for subsequent gas distribution analysis, extraction decision-making and other operations.

[0021] Step S2: Performing gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information.

[0022] Specifically, gas distribution analysis is the process of analyzing the spatial distribution of gas concentration in the goaf (the space left after coal mining). The purpose is to understand the concentration and concentration gradient of gas at different locations.

[0023] Using the collected gas concentration data, numerical simulation methods, such as finite element analysis software, are used to build a numerical model of the goaf, and the received gas concentration sensor information is marked in the model according to its corresponding detection position. At the same time, some known goaf geological structure, ventilation conditions and other parameters may be combined. For example, if the rock permeability, ventilation wind direction and speed of the goaf are known, this information is input into the analysis model as boundary conditions, and the distribution of gas in the goaf is obtained by calculation, that is, the first gas concentration distribution information.

[0024] By performing gas distribution analysis on multiple gas concentration sensor information, we can intuitively understand the gas distribution and provide a scientific basis for subsequent gas extraction decisions.

[0025] Step S3: When the first gas concentration distribution information meets the extraction activation condition, multiple gas extraction pressures are configured for multiple gas extraction tunnel openings.

[0026] Specifically, the extraction activation conditions are some pre-set thresholds or rules for parameters such as gas concentration distribution and gas outflow rate. When the gas distribution meets these conditions, it is considered necessary to extract the gas. The gas extraction tunnel entrance is the entrance of the channel connecting the goaf and the gas extraction equipment, and is the starting point for the gas to be extracted from the goaf. The gas extraction pressure is the pressure applied at the gas extraction tunnel entrance, which creates a pressure difference to cause the gas to flow from the goaf to the extraction equipment.

[0027] The first gas concentration distribution information obtained is compared with the pre-set extraction activation conditions. If the conditions are met, the appropriate gas extraction pressure is configured for multiple gas extraction tunnel openings using a pressure configuration algorithm (which can be based on empirical formulas or numerical simulation results) based on the size of the goaf, gas outflow prediction, etc. For example, for larger goafs and larger gas outflows, a higher extraction pressure will be configured at the gas extraction tunnel openings.

[0028] Extraction decisions are made based on the gas distribution and preliminary extraction pressure configuration is performed, thus achieving dynamic adjustment of gas extraction.

[0029] Step S4: Perform preset time extraction prediction on the multiple gas concentration sensor information and the multiple gas extraction pressures to obtain multiple gas concentration prediction information.

[0030] Specifically, the preset duration is a pre-set time length, which can be determined according to the time interval between two planned extractions, such as 6 hours or 24 hours. The preset duration extraction prediction refers to the prediction of the change of gas concentration after the preset duration based on the current gas concentration sensor information and gas extraction pressure. Preset duration extraction prediction can be performed on multiple gas concentration sensor information and multiple gas extraction pressures using time series analysis methods or regression analysis, machine learning algorithms and other prediction methods. Taking time series analysis as an example, the gas concentration sensor information and gas extraction pressure data in the past period of time are regarded as a time series, and the gas concentration in the future preset duration is predicted using a statistical model (such as an ARIMA model). For each gas extraction tunnel, a prediction is made based on its corresponding gas concentration sensor information and gas extraction pressure to obtain the gas concentration prediction information corresponding to the gas extraction tunnel, and finally multiple gas concentration prediction information is obtained in combination. For example, based on the gas concentration and extraction pressure data of the past 24 hours, the changes in gas concentration near each gas extraction tunnel in the next 6 hours can be predicted.

[0031] By predicting the extraction time at a preset duration, the changing trend of gas concentration can be estimated in advance, providing forward-looking data support for subsequent further gas distribution analysis and extraction decision adjustments.

[0032] Step S5: Perform gas distribution analysis on the multiple gas concentration prediction information to obtain second gas concentration distribution information.

[0033] Specifically, similar to step S2, a numerical simulation analysis method is also used to perform a gas distribution analysis on the multiple gas concentration prediction information obtained by prediction to obtain the second gas concentration distribution information. This second gas concentration distribution information is the gas concentration distribution information obtained based on the prediction model, and is used to evaluate the effect of future gas extraction. By performing a gas distribution analysis on multiple gas concentration prediction information, the gas distribution after extraction and the passage of time can be further understood, providing a more accurate basis for the final extraction decision adjustment.

[0034] Step S6: When the second gas concentration distribution information does not satisfy the extraction activation condition, the multiple gas extraction pressures and the second gas concentration distribution information are sent to the client.

[0035] Specifically, the client refers to a terminal device or software platform that can receive and process information related to gas extraction, which may be a computer in the coal mine's monitoring center, or a monitoring application on the mobile device of the relevant manager. When the second gas concentration distribution information does not meet the extraction activation conditions, multiple gas extraction pressures and second gas concentration distribution information are sent to the client through network communication (such as Ethernet, WiFi, etc.) to achieve feedback of gas extraction information, so that relevant personnel can timely understand the status of gas extraction in the goaf, so as to make subsequent management decisions or monitoring operations, and ensure the flexibility and effectiveness of gas extraction management.

[0036] Further, step S2 includes:

[0037] Step S21: obtaining a three-dimensional model of the goaf, wherein the plurality of gas concentration sensing information has a plurality of gas concentration detection position identifiers in the three-dimensional model of the goaf.

[0038] Step S22: Obtain wind vector information of the goaf.

[0039] Step S23: According to the wind vector information of the goaf, the multiple gas concentration sensor information and the multiple gas concentration detection position identifiers are analyzed on the three-dimensional model of the goaf to obtain the first gas concentration distribution information.

[0040] Specifically, the goaf 3D model is a model obtained by digitally modeling the 3D spatial structure of the goaf (the space left after coal mining), which can intuitively present the shape, size, geological structure and other spatial characteristics of the goaf, such as the ups and downs of the roof and floor of the goaf, the distribution of faults, etc. The gas concentration detection position mark is the specific location point where the gas concentration sensor detects the gas concentration marked in the goaf 3D model. These marks help to accurately associate the gas concentration information with the corresponding spatial position in the 3D model.

[0041] Use 3D modeling software, such as 3DMAX or Surpac and other specialized geological modeling software, to build a 3D model of the goaf. Use the modeling software to analyze and process the geological exploration data of the goaf (such as drilling data, seismic wave data, etc.) to construct the 3D structure of the goaf. Then, according to the installation location information of the gas concentration sensor, mark the corresponding gas concentration detection position mark in the 3D model. For example, gas concentration sensors are installed in different corners of the goaf and at different heights near the coal seam. The corresponding positions of these sensors are marked in the 3D model to generate gas concentration detection position marks. The 3D model of the goaf provides an intuitive and accurate spatial framework for subsequent gas distribution analysis, and enables the gas concentration information to accurately correspond to the spatial position of the goaf, which helps to more accurately analyze the distribution of gas in the goaf.

[0042] The wind vector information of the goaf is the information describing the direction and size of the air flow in the goaf, expressed in the form of a vector. The wind vector contains two key elements: the size of the wind speed and the wind direction. For example, in a certain area of ​​the goaf, the wind vector indicates that the wind speed is 2m / s and the wind direction is southeast. Use an anemometer and other measuring equipment to measure in or around the goaf, and then organize the measured data to obtain information in the form of a vector to obtain the wind vector information of the goaf. It is also possible to simulate and calculate the wind vector information in the goaf through numerical simulation software (such as FLUENT, etc.) based on the ventilation system parameters of the goaf (such as the layout of the ventilation ducts, the power of the fan, etc.) and the geometric shape of the goaf. Wind is one of the important factors affecting the distribution of gas in the goaf. Mastering the wind vector information of the goaf can better understand the diffusion and migration laws of gas and provide key environmental factors for gas distribution analysis.

[0043] The goaf wind vector information, multiple gas concentration sensor information and multiple gas concentration detection location identifiers are input into the three-dimensional model, and the gas distribution analysis is performed through the CFD simulation tool to simulate the diffusion path and concentration distribution of gas in the goaf, thereby obtaining the first gas concentration distribution information. For example, in a complex goaf, there are multiple gas sources with different concentrations. Through CFD simulation, the concentration distribution of gas in the entire goaf under the action of wind can be accurately calculated. By analyzing the comprehensive information from various aspects, the first gas concentration distribution information that is more in line with the actual situation can be obtained, which provides a scientific and accurate basis for judging whether gas extraction is necessary and formulating extraction strategies.

[0044] Further, step S23 includes:

[0045] Step S231: randomly configuring a first gas emission position set and a first gas emission concentration set in the three-dimensional model of the goaf.

[0046] Step S232: Performing a gas propagation simulation on the first gas outburst position set and the first gas outburst concentration set in the three-dimensional model of the goaf according to the wind vector information of the goaf, to obtain first simulated gas concentration distribution information.

[0047] Step S233: When the first simulated gas concentration distribution information satisfies the plurality of gas concentration sensing information at the plurality of gas concentration detection position identifiers, the first simulated gas concentration distribution information is added to the first gas concentration distribution information.

[0048] Specifically, the gas emission location set refers to the location set of gas release sources simulated in the three-dimensional model of the goaf, which represent the locations where gas may be released from the coal seam or rock formation. The gas emission concentration set refers to the gas concentration value set corresponding to the gas emission location set, which represents the gas concentration simulated at each emission location. In the three-dimensional model of the goaf, multiple gas emission locations and corresponding concentrations are randomly configured. These gas emission locations and corresponding concentrations constitute the first gas emission location set and the first gas emission concentration set.

[0049] Using the three-dimensional model of the goaf and the configured first gas outburst location set and the first gas outburst concentration set, combined with wind vector information, the gas spreading process in the goaf is simulated by computer. Computational fluid dynamics software such as FLUENT is used to calculate the gas concentration distribution at various locations in the goaf when the gas spreads in the goaf under different wind speeds and wind directions, thereby obtaining the first simulated gas concentration distribution information. By simulating the diffusion process of gas with wind flow, a predicted gas concentration distribution map is provided, which provides key prediction data for assessing gas risks and formulating extraction strategies.

[0050] The first simulated gas concentration distribution information is compared one by one with the multiple gas concentration sensor information corresponding to the multiple gas concentration detection position identifiers. When the concentration deviation between the simulation data and the actual data at each sensor position is within the allowable range, the simulation result is considered to be credible and added to the first gas concentration distribution information. For example, the simulated gas concentration at a specific position in the three-dimensional model of the goaf is compared with the gas concentration actually detected by the gas concentration sensor at that position. If the error between the two is within a pre-set range (such as the error does not exceed 5%), it is considered that the first simulated gas concentration distribution information satisfies the multiple gas concentration sensor information at the multiple gas concentration detection position identifiers, and the first simulated gas concentration distribution information is added to the first gas concentration distribution information. By matching and verifying the simulation results with the actual sensor data, the credibility of the simulation results is ensured, and the subsequent extraction pressure configuration and optimization are ensured to be based on accurate data, thereby improving the control accuracy and safety management level of coal mine gas extraction.

[0051] Further, step S4 includes:

[0052] Step S41: Obtain a first gas extraction prediction model associated with a first gas extraction tunnel, perform a preset duration extraction prediction on a first gas concentration sensor information of the multiple gas concentration sensor information and a first gas extraction pressure of the multiple gas extraction pressures, and obtain first gas concentration prediction information.

[0053] Step S42: until the Nth gas extraction prediction model associated with the Nth gas extraction tunnel is obtained, a preset duration extraction prediction is performed on the Nth gas concentration sensor information of the multiple gas concentration sensor information and the Nth gas extraction pressure of the multiple gas extraction pressures to obtain the Nth gas concentration prediction information.

[0054] Step S43: constructing the plurality of gas concentration prediction information according to the first gas concentration prediction information to the Nth gas concentration prediction information.

[0055] Specifically, the first gas extraction tunnel is a specific tunnel among many gas extraction tunnels and is one of the channels through which gas is extracted from the goaf. The first gas extraction prediction model is a model specially established for the first gas extraction tunnel for predicting changes in gas concentration. This model is constructed based on factors such as specific parameters of the tunnel (such as tunnel length, diameter, ventilation conditions, etc.) and physical properties of gas. The first gas concentration sensor information is the gas concentration sensor information related to the first gas extraction tunnel among multiple gas concentration sensor information. The first gas extraction pressure is the gas extraction pressure applied to the tunnel entrance corresponding to the first gas extraction tunnel among multiple gas extraction pressures.

[0056] For the first gas extraction tunnel, a pre-built first gas extraction prediction model associated therewith is obtained. The first gas concentration sensor information and the first gas extraction pressure are input as input data into the first gas extraction prediction model to perform extraction prediction for a preset time. The model uses a machine learning algorithm to predict the gas concentration of the first gas extraction tunnel after the preset time, and outputs the first gas concentration prediction information.

[0057] The Nth gas extraction tunnel represents any tunnel in the gas extraction tunnel, where N is a variable representing the tunnel sequence number, covering all gas extraction tunnels that need to be analyzed. The Nth gas extraction prediction model is a model corresponding to the Nth gas extraction tunnel for predicting changes in gas concentration. Its construction method is similar to that of the first gas extraction prediction model, but the parameters are configured based on the actual situation of the Nth gas extraction tunnel. The Nth gas concentration sensor information is the gas concentration sensor information related to the Nth gas extraction tunnel. The Nth gas extraction pressure is the gas extraction pressure applied at the entrance of the Nth gas extraction tunnel.

[0058] Similarly, for each Nth gas extraction tunnel, obtain its corresponding Nth gas extraction prediction model. Then input the Nth gas concentration sensor information and the Nth gas extraction pressure into the model, perform extraction prediction for a preset duration, and obtain the Nth gas concentration prediction information. For example, if there are 5 gas extraction tunnels, it is necessary to use the gas extraction prediction model associated with each tunnel to perform extraction prediction for a preset duration and obtain 5 corresponding gas concentration prediction information.

[0059] The first gas concentration prediction information to the Nth gas concentration prediction information are combined in the order of the lane numbers to construct multiple gas concentration prediction information. By integrating the gas concentration prediction information of each lane, the gas concentration prediction of the entire gas extraction system after the preset extraction time is obtained, so that the implementation effect of the extraction strategy can be predicted and evaluated, and the optimization adjustment of the gas extraction strategy can be guided.

[0060] Furthermore, the first gas extraction prediction model construction step includes:

[0061] Step 1: After the first gas extraction tunnel is constructed, the first gas extraction tunnel topology is obtained.

[0062] Step 2: Collect a drainage pressure record data set that meets the first gas drainage tunnel topology and a drainage tunnel inlet gas concentration time series record data.

[0063] Step three: Cut the gas concentration time series record data at the inlet of the extraction tunnel according to the preset time length to obtain an extraction pressure record data set, a gas concentration record data set at the inlet of the extraction tunnel at the first moment, and a gas concentration record data set at the inlet of the extraction tunnel at the second moment, wherein the first moment and the second moment are separated by the preset time length.

[0064] Step 4: Construct the first gas extraction prediction model based on the extraction pressure record data set, the gas concentration record data set at the extraction tunnel entrance at the first moment, and the gas concentration record data set at the extraction tunnel entrance at the second moment.

[0065] Specifically, taking the first gas extraction prediction model associated with the first gas extraction roadway as an example, the construction process of the first gas extraction prediction model is described in detail. The construction process of the gas extraction prediction model associated with other gas extraction roadways is similar, and the construction process of the first gas extraction prediction model can be referred to.

[0066] The topology of the first gas extraction roadway is a topological structure that describes the internal structural relationship and connection mode of the first gas extraction roadway, including information such as the branching of the roadway, the location of the connected goaf, and the connection relationship with other roadways or ventilation systems. In the process of building a three-dimensional model of the goaf, when the first gas extraction roadway is built, the topology of the first gas extraction roadway is drawn according to the model data of the first gas extraction roadway. The topology of the first gas extraction roadway can provide a basic framework for the subsequent gas extraction related data collection and analysis, and clarify the location relationship and data association relationship of data collection.

[0067] The drainage pressure record data set is a data set formed by recording the drainage pressure of the first gas drainage tunnel at different time points. The drainage tunnel entrance gas concentration time series record data is data recording the gas concentration at the entrance of the first gas drainage tunnel in chronological order.

[0068] The pressure sensor and gas concentration sensor installed at the entrance of the first gas extraction tunnel are used to perform sensor acquisition on the first gas extraction tunnel, and the extraction pressure record data set of the first gas extraction tunnel topology and the gas concentration time series record data at the entrance of the extraction tunnel are collected and recorded. These two data sets provide the original data materials for the subsequent construction of the gas extraction prediction model and are the basic data source for model construction.

[0069] The collected gas concentration time series record data are divided according to the preset time length to obtain the gas concentration data at different time points, and the start time and end time are determined according to the preset time length, and then the data at the start time are extracted to form the gas concentration record data set at the entrance of the extraction tunnel at the first moment, and the data at the end time (the time point after the preset time length) are extracted to form the gas concentration record data set at the entrance of the extraction tunnel at the second moment.

[0070] According to the extraction pressure record data set, the gas concentration record data set at the entrance of the extraction tunnel at the first moment, and the gas concentration record data set at the entrance of the extraction tunnel at the second moment, the first gas extraction prediction model is constructed by applying algorithms such as statistics, machine learning or time series analysis. This model will learn the relationship between the gas concentration and the extraction pressure of the first gas extraction tunnel, and predict the future gas concentration under a given extraction pressure. Exemplarily, the model is constructed by regression analysis method. The extraction pressure record data set is used as the independent variable x, the gas concentration record data set at the entrance of the extraction tunnel at the first moment is used as the initial state y1, and the gas concentration record data set at the entrance of the extraction tunnel at the second moment is used as the target state y2. The relationship between gas concentration and extraction pressure is described by establishing a regression equation (such as a linear regression equation y2=ax+by1+c, where a, b, c are coefficients to be calculated). Using statistical analysis software, the coefficients in the regression equation are solved according to the data in the extraction pressure record data set, the gas concentration record data set at the entrance of the extraction tunnel at the first moment, and the gas concentration record data set at the entrance of the extraction tunnel at the second moment, so as to construct the first gas extraction prediction model. The first gas extraction prediction model constructed can predict the gas concentration after a preset period of time based on the extraction pressure and initial gas concentration, providing an effective tool for predicting the effect of gas extraction.

[0071] Furthermore, when the second gas concentration distribution information satisfies the extraction activation condition, the multiple gas extraction pressures are updated to obtain multiple gas extraction update pressures; and a cyclic analysis is performed based on the multiple gas extraction update pressures.

[0072] Specifically, the gas extraction update pressure is the pressure value obtained after updating the original multiple gas extraction pressures when the extraction activation conditions are met according to the second gas concentration distribution information. These updated pressures can better adapt to the gas extraction needs than the original multiple gas extraction pressures to achieve a more effective gas extraction effect. Comparing the second gas concentration distribution information with the extraction activation conditions, when the second gas concentration distribution information meets the extraction activation conditions, the multiple gas extraction pressures are updated according to the preset control rules. This preset control rule can be based on the empirical relationship between gas concentration and extraction pressure or the relationship obtained through numerical simulation. For example, the extraction pressure is linearly increased according to the proportion of the gas concentration exceeding the threshold, and the extraction pressure at each gas extraction tunnel entrance is adjusted and updated. By updating the gas extraction pressure, it is possible to more effectively cope with the current gas concentration distribution and improve the efficiency and safety of gas extraction.

[0073] According to multiple gas extraction update pressures, repeat the previous analysis steps, perform gas distribution analysis based on the new gas extraction pressure and gas concentration sensor information, and obtain new gas concentration distribution information. Next, perform extraction prediction based on the new gas concentration distribution information, predict the change of gas concentration under the new extraction pressure, determine the new gas concentration distribution information, and compare it with the extraction activation conditions. When the new gas concentration distribution information meets the extraction activation conditions, the analysis operation is repeated until the gas concentration distribution information does not meet the extraction activation conditions. Through cyclic analysis, the extraction strategy can be continuously adjusted according to the actual situation during the gas extraction process to ensure that the gas concentration distribution after extraction reaches a safe state, improve the efficiency and accuracy of gas extraction, and enhance the safety of the goaf.

[0074] Furthermore, after step S6, the following steps are further included:

[0075] receiving feedback information from the client; and when the feedback information includes execution control, executing control on a plurality of gas extraction tunnels according to the plurality of gas extraction pressures.

[0076] Specifically, after sending multiple gas extraction pressures and the second gas concentration distribution information to the client, feedback information from the client is received. This feedback information contains the client's observation, analysis or control instructions for the gas extraction situation. When the client's feedback information contains an execution control instruction, the instruction content is parsed to extract the control parameters related to the gas extraction pressure. Then, the gas extraction pressure of multiple gas extraction tunnels is adjusted through control equipment (such as electric control valves, fan inverters, etc.). For example, if the instruction requires an increase in the extraction pressure of a gas extraction tunnel, the electric control valve at the entrance of the tunnel is controlled to increase the opening, thereby increasing the gas extraction pressure.

[0077] By receiving and executing client feedback information, human-machine collaborative optimization is achieved, and the accuracy and flexibility of gas extraction are further improved by combining real-time analysis and manual guidance. In order to cope with complex gas distribution conditions, the extraction efficiency and safety are significantly improved.

[0078] In summary, the method for evaluating gas concentration distribution in goaf under extraction conditions provided in the embodiments of the present application has the following technical effects:

[0079] In general, the embodiment of the present application starts with obtaining information from the gas concentration sensor, and after multiple operations such as gas distribution analysis, extraction pressure configuration, and extraction prediction, a complete gas concentration distribution evaluation system for goafs under extraction conditions is constructed. The system fully considers the complex environmental factors of goafs, such as three-dimensional spatial structure, wind vector, etc., and realizes accurate analysis and prediction of gas concentration distribution. By dynamically configuring and updating the extraction pressure according to the gas distribution, and realizing refined control of tunnel gas extraction through information interaction with the client, the efficiency and safety of gas extraction are significantly improved, the risk of gas accumulation is reduced, and reliable technical guarantees are provided for safe production in coal mines.

[0080] Embodiment 2, as Figure 2 As shown, the embodiment of the present application provides a gas concentration distribution evaluation system in goaf under extraction conditions, the system comprising:

[0081] The gas concentration sensing module 10 is used to communicate with a plurality of gas concentration sensors and receive a plurality of gas concentration sensing information.

[0082] The first distribution analysis module 20 is used to perform gas distribution analysis on the multiple gas concentration sensor information to obtain first gas concentration distribution information.

[0083] The drainage pressure configuration module 30 is used to configure multiple gas drainage pressures for multiple gas drainage tunnel openings when the first gas concentration distribution information meets the drainage activation condition.

[0084] The extraction prediction module 40 is used to perform extraction prediction for a preset time period on the multiple gas concentration sensor information and the multiple gas extraction pressures to obtain multiple gas concentration prediction information.

[0085] The second distribution analysis module 50 is used to perform gas distribution analysis on the multiple gas concentration prediction information to obtain second gas concentration distribution information.

[0086] The concentration feedback module 60 is configured to send the plurality of gas drainage pressures and the second gas concentration distribution information to a client when the second gas concentration distribution information does not satisfy the drainage activation condition.

[0087] Furthermore, the system described in the embodiment of the present application further includes a cycle analysis module, and the cycle analysis module is used to perform the following steps:

[0088] When the second gas concentration distribution information satisfies the extraction activation condition, the multiple gas extraction pressures are updated to obtain multiple gas extraction update pressures, and cyclic analysis is performed according to the multiple gas extraction update pressures.

[0089] Furthermore, the first distribution analysis module 20 in the embodiment of the present application is also used to perform the following steps:

[0090] A three-dimensional model of a goaf is obtained, wherein the multiple gas concentration sensor information has multiple gas concentration detection position identifiers in the three-dimensional model of the goaf; wind vector information of the goaf is obtained; and based on the wind vector information of the goaf, gas distribution analysis is performed on the multiple gas concentration sensor information and the multiple gas concentration detection position identifiers in the three-dimensional model of the goaf to obtain the first gas concentration distribution information.

[0091] Furthermore, the first distribution analysis module 20 in the embodiment of the present application is also used to perform the following steps:

[0092] A first gas outburst position set and a first gas outburst concentration set are randomly configured in the three-dimensional model of the goaf; a gas propagation simulation is performed on the first gas outburst position set and the first gas outburst concentration set in the three-dimensional model of the goaf according to the wind vector information of the goaf to obtain first simulated gas concentration distribution information; when the first simulated gas concentration distribution information satisfies the multiple gas concentration sensing information at the multiple gas concentration detection position identifiers, the first simulated gas concentration distribution information is added to the first gas concentration distribution information.

[0093] Furthermore, the extraction prediction module 40 of the embodiment of the present application is also used to perform the following steps:

[0094] Obtain a first gas extraction prediction model associated with a first gas extraction tunnel, perform a preset-duration extraction prediction on the first gas concentration sensor information of the multiple gas concentration sensor information and the first gas extraction pressure of the multiple gas extraction pressures, and obtain first gas concentration prediction information; until an Nth gas extraction prediction model associated with an Nth gas extraction tunnel is obtained, perform a preset-duration extraction prediction on the Nth gas concentration sensor information of the multiple gas concentration sensor information and the Nth gas extraction pressure of the multiple gas extraction pressures, and obtain Nth gas concentration prediction information; construct the multiple gas concentration prediction information based on the first gas concentration prediction information until the Nth gas concentration prediction information.

[0095] Furthermore, the system described in the embodiment of the present application is also used to construct a first gas extraction prediction model, and the execution steps include:

[0096] When the first gas extraction tunnel is constructed, the first gas extraction tunnel topology is obtained; the extraction pressure record data set and the gas concentration time series record data at the extraction tunnel entrance that meet the first gas extraction tunnel topology are collected; the gas concentration time series record data at the extraction tunnel entrance are cut according to a preset time length to obtain an extraction pressure record data set, a gas concentration record data set at the extraction tunnel entrance at the first moment, and a gas concentration record data set at the extraction tunnel entrance at the second moment, wherein the first moment and the second moment are separated by the preset time length; the first gas extraction prediction model is constructed according to the extraction pressure record data set, the gas concentration record data set at the extraction tunnel entrance at the first moment, and the gas concentration record data set at the extraction tunnel entrance at the second moment.

[0097] Furthermore, the concentration feedback module 60 of the embodiment of the present application is also used to perform the following steps:

[0098] receiving feedback information from the client; and when the feedback information includes execution control, executing control on a plurality of gas extraction tunnels according to the plurality of gas extraction pressures.

[0099] Through the detailed description of the method for evaluating the gas concentration distribution in the goaf under extraction conditions mentioned above in this specification, those skilled in the art can clearly understand the system for evaluating the gas concentration distribution in the goaf under extraction conditions in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0100] Example three, based on the same inventive concept of the method for evaluating the gas concentration distribution in goaf under extraction conditions in the aforementioned example one, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor, the various steps of the example of the method for evaluating the gas concentration distribution in goaf under extraction conditions are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0101] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating gas concentration distribution in goaf under extraction conditions, characterized in that: The system is applied to a gas concentration distribution evaluation system of a goaf under extraction conditions, wherein the system is in communication connection with a plurality of gas concentration sensors, wherein the plurality of gas concentration sensors are deployed at a plurality of gas extraction tunnel openings, and the plurality of gas extraction tunnel openings are in communication with the goaf, and comprises: Communicate with a plurality of gas concentration sensors and receive a plurality of gas concentration sensing information; Performing gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information; When the first gas concentration distribution information satisfies the extraction activation condition, multiple gas extraction pressures are configured for multiple gas extraction tunnel openings; Performing preset time extraction prediction on the multiple gas concentration sensor information and the multiple gas extraction pressures to obtain multiple gas concentration prediction information; Performing gas distribution analysis on the plurality of gas concentration prediction information to obtain second gas concentration distribution information; When the second gas concentration distribution information does not satisfy the extraction activation condition, the multiple gas extraction pressures and the second gas concentration distribution information are sent to the client.

2. The method for evaluating gas concentration distribution in goaf under extraction conditions according to claim 1, characterized in that: Performing gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information includes: Obtaining a three-dimensional model of a goaf, wherein the plurality of gas concentration sensing information has a plurality of gas concentration detection position identifiers in the three-dimensional model of the goaf; Obtain wind vector information of goaf; According to the wind vector information of the goaf, the gas concentration sensor information and the gas concentration detection position identifiers are analyzed on the three-dimensional model of the goaf to obtain the first gas concentration distribution information.

3. The method for evaluating gas concentration distribution in goaf under extraction conditions as claimed in claim 2, characterized in that: According to the goaf wind vector information, the plurality of gas concentration sensor information and the plurality of gas concentration detection position identifiers are analyzed for gas distribution in the goaf three-dimensional model to obtain the first gas concentration distribution information, including: Randomly configuring a first gas emission position set and a first gas emission concentration set in the three-dimensional model of the goaf; According to the goaf wind vector information, a gas spreading simulation is performed on the first gas emission position set and the first gas emission concentration set in the goaf three-dimensional model to obtain first simulated gas concentration distribution information; When the first simulated gas concentration distribution information satisfies the plurality of gas concentration sensing information at the plurality of gas concentration detection position identifiers, the first simulated gas concentration distribution information is added to the first gas concentration distribution information.

4. The method for evaluating gas concentration distribution in goaf under extraction conditions according to claim 1, characterized in that: The plurality of gas concentration sensor information and the plurality of gas extraction pressures are predicted for a preset time period to obtain a plurality of gas concentration prediction information, including: Obtaining a first gas extraction prediction model associated with a first gas extraction tunnel, performing a preset time extraction prediction on a first gas concentration sensor information of the plurality of gas concentration sensor information and a first gas extraction pressure of the plurality of gas extraction pressures, and obtaining first gas concentration prediction information; Until the Nth gas extraction prediction model associated with the Nth gas extraction roadway is obtained, the Nth gas concentration sensor information of the plurality of gas concentration sensor information and the Nth gas extraction pressure of the plurality of gas extraction pressures are subjected to a preset time extraction prediction to obtain the Nth gas concentration prediction information; The plurality of gas concentration prediction information are constructed based on the first gas concentration prediction information to the Nth gas concentration prediction information.

5. The method for evaluating gas concentration distribution in goaf under extraction conditions as claimed in claim 4, characterized in that: The first gas extraction prediction model construction step includes: When the first gas extraction tunnel is constructed, a first gas extraction tunnel topology is obtained; Collecting a drainage pressure record data set and a drainage tunnel inlet gas concentration time series record data that meet the first gas drainage tunnel topology; Cut the gas concentration time series record data at the entrance of the drainage tunnel according to the preset time length to obtain a drainage pressure record data set, a gas concentration record data set at the entrance of the drainage tunnel at the first moment, and a gas concentration record data set at the entrance of the drainage tunnel at the second moment, wherein the first moment and the second moment are separated by the preset time length; The first gas extraction prediction model is constructed based on the extraction pressure record data set, the gas concentration record data set at the extraction tunnel entrance at the first moment, and the gas concentration record data set at the extraction tunnel entrance at the second moment.

6. The method for evaluating gas concentration distribution in goaf under extraction conditions according to claim 1, characterized in that: Also includes: When the second gas concentration distribution information satisfies the extraction activation condition, the multiple gas extraction pressures are updated to obtain multiple gas extraction update pressures; A cyclic analysis is performed based on the plurality of gas drainage update pressures.

7. The method for evaluating gas concentration distribution in goaf under extraction conditions according to claim 1, characterized in that: When the second gas concentration distribution information does not meet the drainage activation condition, the plurality of gas drainage pressures and the second gas concentration distribution information are sent to a client, and then the method further includes: receiving feedback information from the client; When the feedback information includes execution control, the plurality of gas drainage tunnels are controlled according to the plurality of gas drainage pressures.

8. The gas concentration distribution evaluation system in goaf under extraction conditions is characterized by: include: A gas concentration sensing module, used for communicating with a plurality of gas concentration sensors and receiving a plurality of gas concentration sensing information; A first distribution analysis module, configured to perform gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information; A drainage pressure configuration module, configured to configure multiple gas drainage pressures for multiple gas drainage tunnel openings when the first gas concentration distribution information meets the drainage activation condition; A drainage prediction module, used to perform a preset duration drainage prediction on the multiple gas concentration sensor information and the multiple gas drainage pressures to obtain multiple gas concentration prediction information; A second distribution analysis module, configured to perform gas distribution analysis on the plurality of gas concentration prediction information to obtain second gas concentration distribution information; A concentration feedback module is used to send the multiple gas extraction pressures and the second gas concentration distribution information to the client when the second gas concentration distribution information does not meet the extraction activation condition.

9. The gas concentration distribution evaluation system for goaf under extraction conditions according to claim 8, characterized in that: The system further comprises: A cyclic analysis module is used to update the multiple gas extraction pressures when the second gas concentration distribution information meets the extraction activation condition, obtain multiple gas extraction update pressures, and perform cyclic analysis according to the multiple gas extraction update pressures.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating gas concentration distribution in goaf under extraction conditions as described in any one of claims 1 to 7 are implemented.

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