Evaluation method and system for gas concentration distribution in goaf under extraction condition and medium

Abstract

The application provides a goaf gas concentration distribution evaluation method, system and medium under gas extraction, relates to the technical field of gas extraction, and comprises the following steps: collecting a plurality of gas concentration sensing information for gas distribution analysis, and obtaining first gas concentration distribution information; when the first gas concentration distribution information meets the extraction activation condition, configuring a gas extraction pressure for a plurality of roadway openings; predicting the gas concentration sensing information and the gas extraction pressure, obtaining a plurality of gas concentration prediction information, and performing 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, sending the gas extraction pressure and the second gas concentration distribution information to a client. The application solves the technical problem that the existing technology is difficult to accurately predict the gas distribution state, resulting in coarse roadway extraction control granularity and affecting the gas extraction efficiency, and improves the control accuracy, extraction efficiency and extraction safety of gas extraction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas extraction, in particular to a goaf gas concentration distribution evaluation method, system and medium under extraction conditions. BACKGROUND

[0002] Gas extraction is a key link to ensure the safety production of coal mines. In the process of coal mining, goaf will emit gas, and if effective extraction is not carried out, it is easy to cause safety accidents. The existing gas extraction methods are mainly divided into sealed extraction method, drilling extraction method and roadway extraction method. The sealed extraction method is suitable for relatively closed occasions in the goaf; the drilling extraction method is suitable for the case that the coal seam has good permeability and the gas emission is uniform; the roadway extraction method is the most widely used due to its strong flexibility and wide adaptability, especially in the case of complex roadway layout and dispersed gas emission.

[0003] In the actual mining process, the ventilation conditions between different roadways, the gas emission speed and the change of coal and rock structure will all affect the spatio-temporal distribution of gas concentration, and the existing roadway extraction method usually lacks sufficient monitoring means and cannot fully perceive these factors. The lack of such gas distribution information leads to the blindness of the extraction strategy. Secondly, the dispersion of gas emission further limits the accuracy of extraction control. Due to the significant differences in gas emission in each roadway, the existing method usually uses a unified extraction pressure or flow setting, which not only may lead to insufficient gas extraction, but also may cause energy waste.

[0004] In summary, the roadway extraction method is difficult to effectively respond to the dynamic changes of gas distribution under complex coal mine conditions, the extraction control granularity is relatively coarse, and the fine degree is insufficient, thereby affecting the gas extraction efficiency and posing a potential threat to the safety production of coal mines. SUMMARY

[0005] The present application provides a goaf gas concentration distribution evaluation method, system and medium under extraction conditions, which solves the technical problem that the existing technology is difficult to accurately predict the gas distribution state due to the complex roadway layout and dispersed gas emission, resulting in a relatively coarse roadway extraction control granularity and affecting the gas extraction efficiency, and achieves the technical effect of realizing fine control of gas extraction and further improving the extraction efficiency and extraction safety.

[0006] In view of the above problems, in one aspect, the 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. The system is in communication with a plurality of gas concentration sensors, which are arranged at a plurality of gas extraction roadway openings that are in communication with the goaf. The method comprises: communicating with the plurality of gas concentration sensors and receiving a plurality of gas concentration sensing information; performing gas distribution analysis on the plurality of gas concentration sensing information to obtain first gas concentration distribution information; when the first gas concentration distribution information satisfies an extraction activation condition, configuring a plurality of gas extraction pressures for the plurality of gas extraction roadway openings; performing extraction prediction on the plurality of gas concentration sensing information and the plurality of gas extraction pressures for a preset time period to obtain a plurality of gas concentration prediction information; performing gas distribution analysis on the plurality of gas concentration prediction information to obtain second gas concentration distribution information; and when the second gas concentration distribution information does not satisfy the extraction activation condition, sending the plurality of gas extraction pressures and the second gas concentration distribution information to a client.

[0007] In another aspect, the application also provides a system for evaluating the gas concentration distribution in a goaf under extraction conditions. The system comprises: a gas concentration sensing module for communicating with a plurality of gas concentration sensors and receiving a plurality of gas concentration sensing information; a first distribution analysis module for performing gas distribution analysis on the plurality of gas concentration sensing information to obtain first gas concentration distribution information; an extraction pressure configuration module for configuring a plurality of gas extraction pressures for a plurality of gas extraction roadway openings when the first gas concentration distribution information satisfies an extraction activation condition; an extraction prediction module for performing extraction prediction on the plurality of gas concentration sensing information and the plurality of gas extraction pressures for a preset time period to obtain a plurality of gas concentration prediction information; a second distribution analysis module for performing gas distribution analysis on the plurality of gas concentration prediction information to obtain second gas concentration distribution information; and a concentration feedback module for sending the plurality of gas extraction pressures and the second gas concentration distribution information to a client when the second gas concentration distribution information does not satisfy the extraction activation condition.

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

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

[0010] By communicating with the plurality of gas concentration sensors, the original data of the goaf gas concentration is obtained, and the plurality of gas concentration sensing information is received, providing data support for subsequent analysis and decision-making. By analyzing the plurality of gas concentration sensing information, the distribution of the current gas concentration in the goaf is obtained, that is, the first gas concentration distribution information, which helps to understand the overall state of the goaf gas and determine whether further extraction measures need to be taken. When the first gas concentration distribution information meets the extraction activation condition, a plurality of gas extraction pressures are configured for a plurality of gas extraction roadway openings, and effective extraction measures are started according to the current gas distribution. The plurality of gas concentration sensing information and the plurality of gas extraction pressures are predicted for a preset time length, and a plurality of gas concentration prediction information is obtained to estimate the extraction effect and make the extraction work more forward-looking. The plurality of gas concentration prediction information is analyzed for gas distribution to obtain second gas concentration distribution information, which provides a more accurate basis for the final decision. When the second gas concentration distribution information does not meet the extraction activation condition, the plurality of gas extraction pressures and the second gas concentration distribution information are sent to the client, so that relevant personnel can timely understand the extraction situation for subsequent management and decision-making.

[0011] In summary, the present application realizes fine management of goaf gas extraction by deploying multiple-point gas concentration sensors, combining gas distribution analysis, dynamic extraction pressure adjustment, and preset time length prediction. This scheme not only can monitor the gas concentration distribution in real time, but also can adjust the extraction strategy in advance through prediction, significantly improving the gas extraction efficiency and safety, reducing the risk of gas accumulation, and providing reliable technical support for coal mine safety production.

[0012] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the goaf gas concentration distribution evaluation method under the extraction condition provided by the embodiments of the present application is shown.

[0014] Figure 2 The structure diagram of the goaf gas concentration distribution evaluation system under the extraction condition provided by the embodiments of the present application is shown.

[0015] Explanation of reference signs: 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 embodiment of the application provides a method and system for evaluating gas concentration distribution in a goaf under gas extraction conditions and a medium, and solves the technical problem that in the prior art, due to complex roadway layout and dispersed gas emission, it is difficult to accurately predict the gas distribution state, leading to coarse control granularity of roadway extraction, and affecting the gas extraction efficiency, so as to achieve fine control of gas extraction, and further improve the extraction efficiency and extraction safety.

[0017] Embodiment one, as shown in the figure, the embodiment of the application provides a method for evaluating gas concentration distribution in a goaf under gas extraction conditions, which is applied to a system for evaluating gas concentration distribution in a goaf under gas extraction conditions, the system is in communication connection with a plurality of gas concentration sensors, the plurality of gas concentration sensors are deployed at a plurality of gas extraction roadway openings, the plurality of gas extraction roadway openings are in communication with the goaf, and the method comprises the following steps: Figure 1 Step S1: communicate with the plurality of gas concentration sensors and receive a plurality of gas concentration sensor information.

[0018] Specifically, the gas concentration sensor is a device specially used for detecting the concentration of gas (main component is methane) in the environment, such as an infrared methane sensor or an electrochemical gas sensor. The gas concentration sensor is connected with the plurality of 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 a corresponding signal, and then the system receives these signals and converts them into processable gas concentration sensor information.

[0019] Through communication with the plurality of gas concentration sensors, the collection of gas concentration data is realized, which provides the most basic data source for subsequent gas distribution analysis, extraction decision and other operations.

[0020] Step S2: performing gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information.

[0021] Specifically, the gas distribution analysis is a process of analyzing the distribution of gas concentration in the goaf (space left after coal mining) in space. The purpose is to understand the concentration of gas at different positions, concentration gradient and other conditions.

[0022]

[0023] ​With the collected gas concentration data, a numerical simulation method, such as finite element analysis software, is used to establish a numerical model of the goaf, and the received gas concentration sensing information is marked in the model according to its corresponding detection position. At the same time, some known goaf geological structure, ventilation condition and other parameters may be combined. For example, the rock permeability, ventilation flow direction and speed and other information of the goaf are known, which are input as boundary conditions into the analysis model, and the distribution of gas in the goaf is obtained through calculation, that is, the first gas concentration distribution information.

[0024] Through gas distribution analysis of multiple gas concentration sensing information, the distribution of gas can be intuitively understood, which provides a scientific basis for subsequent gas extraction decision-making.

[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 roadway entrances.

[0026] Specifically, the extraction activation condition is a threshold or rule of some parameters about gas concentration distribution, gas emission rate, etc. that is set in advance. When the gas distribution meets these conditions, it is considered that gas needs to be extracted. The gas extraction roadway entrance is the channel entrance connecting the goaf and the gas extraction equipment, which is the starting position of gas being extracted out of the goaf. The gas extraction pressure is the pressure applied at the gas extraction roadway entrance, which promotes the flow of gas from the goaf to the extraction equipment by creating a pressure difference.

[0027] The obtained first gas concentration distribution information is compared with the pre-set extraction activation condition. If the condition is met, the appropriate gas extraction pressure is configured for multiple gas extraction roadway entrances according to the size of the goaf, gas emission amount prediction and other conditions by using a pressure configuration algorithm (which can be based on an empirical formula or numerical simulation results). For example, for a larger goaf and a larger gas emission amount, a higher extraction pressure is configured at the gas extraction roadway entrance.

[0028] According to the gas distribution, the extraction decision is made and the preliminary extraction pressure configuration is performed, which realizes the dynamic adjustment of gas extraction.

[0029] Step S4: The multiple gas concentration sensing information and the multiple gas extraction pressures are subjected to extraction prediction for a preset time length, and multiple gas concentration prediction information is obtained.

[0030] Specifically, the preset time length is a time length set in advance, which can be determined according to the time interval of the planned two times of extraction, such as 6 hours, 24 hours. The preset time length extraction prediction refers to predicting the change of the gas concentration after the preset time length according to the current gas concentration sensing information and the gas extraction pressure. The preset time length extraction prediction can be performed on multiple gas concentration sensing information and multiple gas extraction pressures using a time series analysis method or a regression analysis, a machine learning algorithm or the like. Taking time series analysis as an example, the gas concentration sensing information and the gas extraction pressure data in the past period of time are regarded as a time series, and a statistical model (such as an ARIMA model) is used to predict the gas concentration in the future preset time length. For each gas extraction roadway, the corresponding gas concentration sensing information and gas extraction pressure are predicted to obtain the gas concentration prediction information corresponding to the gas extraction roadway, and finally multiple gas concentration prediction information is obtained. For example, according to the gas concentration and extraction pressure data in the past 24 hours, the gas concentration change near each gas extraction roadway in the next 6 hours is predicted.

[0031] Through the preset time length extraction prediction, the change trend of the gas concentration can be predicted in advance, which provides prospective data support for subsequent further gas distribution analysis and extraction decision adjustment.

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

[0033] Specifically, similar to step S2, the numerical simulation analysis method is also used to perform gas distribution analysis on the multiple gas concentration prediction information obtained by prediction to obtain second gas concentration distribution information. The second gas concentration distribution information is the gas concentration distribution information obtained based on the prediction model, which is used to evaluate the effect of future gas extraction. By performing gas distribution analysis on the multiple gas concentration prediction information, the gas distribution after extraction and time elapse is further mastered, which provides 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, sending the multiple gas extraction pressures and the second gas concentration distribution information to the client.

[0035] Specifically, the client refers to a terminal device or a software platform that can receive and process gas extraction related information, which can be a monitoring center computer of a coal mine, or a monitoring application on a mobile device of a relevant manager. 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 through network communication (such as Ethernet, WiFi, etc.), realizing the feedback of the gas extraction information, so that the relevant personnel can timely understand the state of the gas extraction in the goaf, so as to make subsequent management decisions or monitoring operations, ensuring the flexibility and effectiveness of the gas extraction management.

[0036] Further, step S2 comprises:

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

[0038] Step S22: obtaining goaf wind vector information.

[0039] Step S23: according to the goaf wind vector information, performing gas distribution analysis on the multiple gas concentration sensing information and the multiple gas concentration detection position identifiers in the goaf three-dimensional model, to obtain the first gas concentration distribution information.

[0040] Specifically, the goaf three-dimensional model is a model obtained by digitizing the three-dimensional spatial structure of the goaf (the space left after coal mining), which can intuitively present the spatial characteristics of the goaf, such as the shape, size, and geological structure, etc., such as the undulation of the roof and floor of the goaf, the distribution of faults, etc. The gas concentration detection position identifier is a specific position point where the gas concentration sensor detects the gas concentration marked in the goaf three-dimensional model. These identifiers help to accurately associate the gas concentration information with the corresponding spatial position in the three-dimensional model.

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

[0042] The goaf wind vector information is information describing the direction and size of air flow in the goaf, and is represented in the form of a vector. The wind vector contains two key elements: wind speed and wind direction. For example, in a certain area of the goaf, the wind vector indicates that the wind speed is 2 m / s and the wind direction is southeast. The wind speed is measured in the goaf or its surroundings using a measuring device such as an anemometer, and the measured data is then sorted to obtain information in the form of a vector, obtaining the goaf wind vector information. The goaf wind vector information can also be calculated by numerical simulation software (such as FLUENT, etc.) according to the ventilation system parameters of the goaf (such as the layout of the ventilation duct, the power of the ventilator, etc.) and the geometric shape of the goaf, etc. Wind is one of the important factors affecting the distribution of gas in the goaf, and mastering the goaf wind vector information can better understand the diffusion and migration rules of gas, and provide key environmental factor basis for gas distribution analysis.

[0043] The goaf wind vector information, the plurality of gas concentration sensing information, and the plurality of gas concentration detection position identifiers are input into the three-dimensional model, and the CFD simulation tool is used to analyze the gas distribution, simulate the diffusion path and concentration distribution of the gas in the goaf, and thus obtain the first gas concentration distribution information. For example, in a complex goaf with multiple gas sources of different concentrations, the CFD simulation can accurately calculate the concentration distribution of the gas in the entire goaf under the action of wind. By analyzing various information, the first gas concentration distribution information that is more in line with the actual situation can be obtained, providing a scientific and accurate basis for determining whether gas extraction is needed 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 goaf three-dimensional model.

[0046] Step S232: Simulating the spread of gas in the goaf three-dimensional model according to the goaf wind vector information for the first gas emission position set and the first gas emission concentration set, and obtaining 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 position set refers to a set of positions of simulated gas release sources in the three-dimensional model of the goaf, which represent the places where gas is likely to be released from the coal seam or rock stratum. The gas emission concentration set refers to a set of gas concentration values corresponding to the gas emission position set, which represent the simulated gas concentration at each emission position. In the three-dimensional model of the goaf, a plurality of gas emission positions and corresponding concentrations are randomly arranged. These gas emission positions and corresponding concentrations constitute a first gas emission position set and a first gas emission concentration set.

[0049] Using the three-dimensional model of the goaf and the arranged first gas emission position set and first gas emission concentration set, combined with the wind vector information, a computer simulation of the gas spreading process in the goaf is performed. Using computational fluid dynamics software such as FLUENT, the gas concentration distribution at each position in the goaf when the gas spreads under different wind speed and direction conditions is calculated, thereby obtaining first simulated gas concentration distribution information. By simulating the diffusion process of gas flow with the wind, a predicted gas concentration distribution map is provided, which provides key prediction data for assessing gas risk and developing extraction strategies.

[0050] The first simulated gas concentration distribution information is compared with the plurality of gas concentration sensor information corresponding to the plurality of gas concentration detection position identifiers one by one. 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 reliable, and it is added to the first gas concentration distribution information. For example, the simulated gas concentration at a certain position in the three-dimensional model of the goaf is compared with the actual gas concentration detected by the gas concentration sensor at that position. If the error between the two is within the pre-set range (such as an error of no more than 5%), it is considered that the first simulated gas concentration distribution information satisfies the plurality of gas concentration sensor information at the plurality of 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 result with the actual sensor data, the credibility of the simulation result is ensured, and the subsequent extraction pressure configuration and optimization are 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: obtaining a first gas extraction prediction model associated with the first gas extraction roadway, performing a preset time length extraction prediction on the first gas concentration sensor information of the plurality of gas concentration sensor information and the first gas extraction pressure of the plurality of gas extraction pressures, and obtaining first gas concentration prediction information.

[0053] Step S42: Until the Nth gas extraction tunnel correlation Nth gas extraction prediction model is obtained, the Nth gas concentration sensing information of the plurality of gas concentration sensing information and the Nth gas extraction pressure of the plurality of gas extraction pressures are subjected to extraction prediction for a preset time length, and the Nth gas concentration prediction information is obtained.

[0054] Step S43: According to the first gas concentration prediction information to the Nth gas concentration prediction information, the plurality of gas concentration prediction information is constructed.

[0055] Specifically, the first gas extraction tunnel is one specific tunnel in the numerous 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 to predict the change of gas concentration. This model is constructed based on specific parameters of the tunnel (such as tunnel length, diameter, ventilation condition, etc.) and physical properties of gas and other factors. The first gas concentration sensing information is the gas concentration sensing information related to the first gas extraction tunnel among the plurality of gas concentration sensing information. The first gas extraction pressure is the gas extraction pressure applied to the tunnel mouth corresponding to the first gas extraction tunnel among the plurality of gas extraction pressures.

[0056] For the first gas extraction tunnel, the first gas extraction prediction model associated therewith is obtained in advance. The first gas concentration sensing information and the first gas extraction pressure are input into the first gas extraction prediction model as input data for extraction prediction for a preset time length. The model predicts the gas concentration of the first gas extraction tunnel after a preset time length using a machine learning algorithm, and outputs the first gas concentration prediction information.

[0057] The Nth gas extraction tunnel represents any one of the gas extraction tunnels, where N is a variable representing the tunnel 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 the change of gas concentration, which is constructed in a similar manner to 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 sensing information is the gas concentration sensing information related to the Nth gas extraction tunnel. The Nth gas extraction pressure is the gas extraction pressure applied to the Nth gas extraction tunnel mouth.

[0058] Similarly, for each Nth gas extraction tunnel, its corresponding Nth gas extraction prediction model is obtained. Then the Nth gas concentration sensing information and the Nth gas extraction pressure are input into the model for extraction prediction for a preset time length, and the Nth gas concentration prediction information is obtained. For example, if there are 5 gas extraction tunnels, extraction prediction for a preset time length needs to be performed using the gas extraction prediction model associated with each tunnel, and 5 corresponding gas concentration prediction information is obtained.

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

[0060] Further, the first gas extraction prediction model construction step comprises:

[0061] Step one: after the first gas extraction roadway is constructed, a first gas extraction roadway topology is obtained.

[0062] Step two: a set of extraction pressure record data and a set of time series record data of gas concentration at the entrance of the extraction roadway that meet the first gas extraction roadway topology are collected.

[0063] Step three: the set of time series record data of gas concentration at the entrance of the extraction roadway is cut according to a preset length of time, and a set of extraction pressure record data, a set of first-time extraction roadway entrance gas concentration record data and a set of second-time extraction roadway entrance gas concentration record data are obtained, wherein the first time and the second time are separated by the preset length of time.

[0064] Step four: the first gas extraction prediction model is constructed according to the set of extraction pressure record data, the set of first-time extraction roadway entrance gas concentration record data and the set of second-time extraction roadway entrance gas concentration record data.

[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 to that of the first gas extraction prediction model, and can be referred to the construction process of the first gas extraction prediction model.

[0066] The first gas extraction roadway topology is a kind of topology structure describing the internal structure relationship and connection mode of the first gas extraction roadway, which contains the branching condition of the roadway, the connected goaf position, the connection relationship with other roadways or ventilation system and other information. In the process of constructing the goaf three-dimensional model, after the first gas extraction roadway is constructed, the first gas extraction roadway topology is drawn according to the model data of the first gas extraction roadway. The first gas extraction roadway topology can provide a basic framework for subsequent gas extraction related data collection and analysis, and clearly define the positional relationship and data correlation of data collection.

[0067] The extraction pressure record data set is a data set formed by recording the extraction pressure of the first gas extraction roadway at different time points. The gas concentration time sequence record data of the extraction roadway entrance is data recorded in time sequence according to the gas concentration at the entrance of the first gas extraction roadway.

[0068] The first gas extraction roadway is sensed and collected by using the pressure sensor and the gas concentration sensor installed at the entrance of the first gas extraction roadway, and the extraction pressure record data set of the first gas extraction roadway topology and the extraction roadway entrance gas concentration time sequence record data are collected and recorded. The two data sets provide original data materials for subsequent construction of a gas extraction prediction model and are the basic data sources for model construction.

[0069] The collected gas concentration time sequence record data is segmented according to a preset time length to obtain gas concentration data at different time points, and the start time and the end time are determined according to the preset time length, and then the data at the start time is extracted to form the first time extraction roadway entrance gas concentration record data set, and the data at the end time (the time point after the preset time length) is extracted to form the second time extraction roadway entrance gas concentration record data set.

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

[0071] Further, when the second gas concentration distribution information satisfies the extraction activation condition, the plurality of gas extraction pressures are updated to obtain a plurality of gas extraction update pressures; and the loop analysis is performed according to the plurality of gas extraction update pressures.

[0072] Specifically, the updated gas drainage pressure is the pressure value obtained by updating multiple original gas drainage pressures when the second gas concentration distribution information meets the drainage activation conditions. These updated pressures are better suited to the gas drainage needs compared to the original pressures, achieving a more effective gas drainage result. By comparing the second gas concentration distribution information with the drainage activation conditions, when the second gas concentration distribution information meets the activation conditions, multiple gas drainage pressures are updated according to preset control rules. These preset control rules can be based on the empirical relationship between gas concentration and drainage pressure or a relationship obtained through numerical simulation. For example, the drainage pressure can be linearly increased according to the proportion of gas concentration exceeding a threshold, adjusting and updating the drainage pressure at each gas drainage roadway entrance. By updating the gas drainage pressure, the current gas concentration distribution can be addressed more effectively, improving the efficiency and safety of gas drainage.

[0073] Based on multiple updated gas drainage pressures, the previous analysis steps are repeated. Gas distribution analysis is performed using the new gas drainage pressure and gas concentration sensor information to obtain new gas concentration distribution information. Next, drainage prediction is performed based on this new gas concentration distribution information to predict changes in gas concentration under the new drainage pressure, thus determining the new gas concentration distribution information and comparing it with the drainage activation conditions. When the new gas concentration distribution information meets the drainage activation conditions, the analysis is repeated until the gas concentration distribution information no longer meets the activation conditions. Through cyclical analysis, the drainage strategy can be continuously adjusted based on the actual situation during the gas drainage process, ensuring that the post-drainage gas concentration distribution reaches a safe state, improving the efficiency and accuracy of gas drainage, and enhancing the safety of the goaf.

[0074] Furthermore, step S6 includes the following:

[0075] Receive feedback information from the client; when the feedback information includes execution control, execute control on multiple gas drainage roadways according to the multiple gas drainage pressures.

[0076] Specifically, after sending multiple gas drainage pressure and second gas concentration distribution information to the client, the system receives feedback from the client. This feedback includes the client's observations, analysis, or control commands regarding the gas drainage situation. When the client's feedback includes a control command, the system parses the command and extracts the control parameters related to the gas drainage pressure. Then, the gas drainage pressure in multiple gas drainage roadways is adjusted using control equipment (such as electric regulating valves, fan frequency converters, etc.). For example, if the command requires increasing the drainage pressure in a certain gas drainage roadway, the electric regulating valve at the entrance of that roadway is opened wider, thereby increasing the gas drainage pressure.

[0077] By receiving and executing client feedback, human-machine collaborative optimization was achieved. Combined with real-time analysis and human guidance, the accuracy and flexibility of gas drainage were further improved. This significantly enhances drainage efficiency and safety to cope with complex gas distribution conditions.

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

[0079] Overall, this application's embodiments begin by acquiring information from gas concentration sensors, and through multiple operations such as gas distribution analysis, extraction pressure configuration, and extraction prediction, construct a complete assessment system for gas concentration distribution in goaf under extraction conditions. This system fully considers the complex environmental factors of the goaf, such as three-dimensional spatial structure and wind vectors, achieving accurate analysis and prediction of gas concentration distribution. By dynamically configuring and updating the extraction pressure based on gas distribution and through information interaction with the client, it achieves refined control of roadway gas extraction, significantly improving gas extraction efficiency and safety, reducing the risk of gas accumulation, and providing reliable technical support for safe coal mine production.

[0080] Example 2, as Figure 2 As shown in the embodiment of this application, a system for assessing the gas concentration distribution in a goaf under extraction conditions is provided. The system includes:

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

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

[0083] The extraction pressure configuration module 30 is used to configure multiple gas extraction pressures for multiple gas extraction roadway entrances when the first gas concentration distribution information meets the extraction activation conditions.

[0084] The extraction prediction module 40 is used to perform preset-duration extraction prediction on the multiple gas concentration sensing information and the multiple gas extraction pressure 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 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.

[0087] Furthermore, the system described in this application embodiment also includes a loop analysis module, which is used to perform the following steps:

[0088] When the second gas concentration distribution information meets the extraction activation condition, the multiple gas extraction pressures are updated to obtain multiple updated gas extraction pressures, and a cyclic analysis is performed based on the multiple updated gas extraction pressures.

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

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

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

[0092] A first set of gas emission locations and a first set of gas emission concentrations are randomly configured in the three-dimensional model of the goaf. Based on the goaf wind vector information, a gas propagation simulation is performed on the first set of gas emission locations and the first set of gas emission concentrations in the three-dimensional model 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 multiple gas concentration detection locations, the first simulated gas concentration distribution information is added to the first gas concentration distribution information.

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

[0094] A first gas drainage prediction model associated with the first gas drainage roadway is obtained. A preset duration drainage prediction is performed on the first gas concentration sensing information of the multiple gas concentration sensing information and the first gas drainage pressure of the multiple gas drainage pressures to obtain first gas concentration prediction information. This process continues until a second gas drainage prediction model associated with the Nth gas drainage roadway is obtained. A preset duration drainage prediction is performed on the Nth gas concentration sensing information of the multiple gas concentration sensing information and the Nth gas drainage pressure of the multiple gas drainage pressures to obtain Nth gas concentration prediction information. Based on the first gas concentration prediction information up to the Nth gas concentration prediction information, the multiple gas concentration prediction information is constructed.

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

[0096] After the first gas drainage roadway is constructed, the topology of the first gas drainage roadway is obtained; a drainage pressure record dataset and a time-series record of gas concentration at the entrance of the drainage roadway that satisfy the topology of the first gas drainage roadway are collected; the time-series record of gas concentration at the entrance of the drainage roadway is segmented according to a preset duration to obtain a drainage pressure record dataset, a first-time gas concentration record dataset at the entrance of the drainage roadway, and a second-time gas concentration record dataset at the entrance of the drainage roadway, wherein the first time and the second time are separated by the preset duration; based on the drainage pressure record dataset, the first-time gas concentration record dataset at the entrance of the drainage roadway, and the second-time gas concentration record dataset at the entrance of the drainage roadway, the first gas drainage prediction model is constructed.

[0097] Furthermore, in this embodiment, the concentration feedback module 60 is also used to perform the following steps:

[0098] Receive feedback information from the client; when the feedback information includes execution control, execute control on multiple gas drainage roadways according to the multiple gas drainage pressures.

[0099] Through the foregoing detailed description of the method for assessing the gas concentration distribution in the goaf under extraction conditions, those skilled in the art can clearly understand that the gas concentration distribution assessment system for the goaf under extraction conditions in this embodiment corresponds to the system disclosed in Embodiment 2. As it is similar to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For relevant details, please refer to the method section.

[0100] In Example 3, based on the same inventive concept as the method for assessing the distribution of gas concentration in the goaf under extraction conditions in Example 1, this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements each step of the above-described method for assessing the distribution of gas concentration in the goaf under extraction conditions and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily 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 this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the distribution of gas concentration in a goaf under the condition of extraction, characterized in that, The application is applied to a goaf gas concentration distribution evaluation system under extraction conditions. The system is in communication connection with a plurality of gas concentration sensors. The plurality of gas concentration sensors are deployed at a plurality of gas extraction roadway openings. The plurality of gas extraction roadway openings are in communication with a goaf. The system comprises: communicating with the plurality of gas concentration sensors and receiving a plurality of gas concentration sensor 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 meets the extraction activation condition, configuring a plurality of gas extraction pressures for the plurality of gas extraction roadway openings; performing a preset time length 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 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 meet the extraction activation condition, sending the plurality of gas extraction pressures and the second gas concentration distribution information to a client; performing a preset time length 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, comprising: obtaining a first gas extraction prediction model associated with a first gas extraction roadway, performing a preset time length extraction prediction on first gas concentration sensor information of the plurality of gas concentration sensor information and first gas extraction pressure of the plurality of gas extraction pressures to obtain first gas concentration prediction information; until obtaining an Nth gas extraction prediction model associated with an Nth gas extraction roadway, performing a preset time length extraction prediction on Nth gas concentration sensor information of the plurality of gas concentration sensor information and Nth gas extraction pressure of the plurality of gas extraction pressures to obtain Nth gas concentration prediction information; constructing the plurality of gas concentration prediction information according to the first gas concentration prediction information to the Nth gas concentration prediction information; the first gas extraction prediction model construction step comprises: after the first gas extraction roadway is constructed, obtaining a first gas extraction roadway topology; collecting extraction pressure record data set and extraction roadway entrance gas concentration time sequence record data that meet the first gas extraction roadway topology; cutting the extraction roadway entrance gas concentration time sequence record data according to a preset time length to obtain extraction pressure record data set, first time extraction roadway entrance gas concentration record data set and second time extraction roadway entrance gas concentration record data set, wherein the first time and the second time are separated by the preset time length; constructing the first gas extraction prediction model according to the extraction pressure record data set, the first time extraction roadway entrance gas concentration record data set and the second time extraction roadway entrance gas concentration record data set; performing gas distribution analysis on the plurality of gas concentration sensor information to obtain first gas concentration distribution information, comprising: obtaining a goaf three-dimensional model, wherein the plurality of gas concentration sensor information has a plurality of gas concentration detection position identifiers in the goaf three-dimensional model; obtaining goaf wind vector information; According to the goaf wind vector information, the multiple gas concentration sensing information and the multiple gas concentration detection position marks are subjected to gas distribution analysis on the goaf three-dimensional model to obtain the first gas concentration distribution information; According to the goaf wind vector information, the multiple gas concentration sensing information and the multiple gas concentration detection position marks are subjected to gas distribution analysis on the goaf three-dimensional model to obtain the first gas concentration distribution information, including: A first gas emission position set and a first gas emission concentration set are randomly configured on the goaf three-dimensional model; According to the goaf wind vector information, the first gas emission position set and the first gas emission concentration set are subjected to gas spread simulation on the goaf three-dimensional model to obtain first simulated gas concentration distribution information; When the first simulated gas concentration distribution information meets the multiple gas concentration sensing information at the multiple gas concentration detection position marks, the first simulated gas concentration distribution information is added to the first gas concentration distribution information; When the second gas concentration distribution information meets the extraction activation condition, the multiple gas extraction pressures are updated to obtain multiple gas extraction updated pressures; According to the multiple gas extraction updated pressures, a cycle analysis is performed.

2. The method for evaluating the gas concentration distribution in the goaf under the drainage condition according to claim 1, characterized in that, 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, and then further comprising: Receiving feedback information of the client; When the feedback information includes execution control, according to the multiple gas extraction pressures, control is performed on multiple gas extraction roadways.

3. The system for evaluating the distribution of gas concentration in a goaf under the condition of extraction, characterized in that, The system is used to perform the method of any one of claims 1-2, comprising: A gas concentration sensing module for communicating with multiple gas concentration sensors and receiving multiple gas concentration sensing information; A first distribution analysis module for performing gas distribution analysis on the multiple gas concentration sensing information to obtain first gas concentration distribution information; An extraction pressure configuration module for configuring multiple gas extraction pressures for multiple gas extraction roadway openings when the first gas concentration distribution information meets an extraction activation condition; An extraction prediction module for performing preset time length extraction prediction 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 for performing gas distribution analysis on the multiple gas concentration prediction information to obtain second gas concentration distribution information; A concentration feedback module for sending 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.

4. The system for evaluating the gas concentration distribution in the goaf under the drainage condition according to claim 3, wherein, The system further comprises: A cycle analysis module for updating the multiple gas extraction pressures to obtain multiple gas extraction updated pressures when the second gas concentration distribution information meets the extraction activation condition, and performing cycle analysis according to the multiple gas extraction updated pressures.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the steps of the goaf gas concentration distribution evaluation method under the drainage condition according to any one of claims 1-2.

Images